Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [66]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[66]:
<matplotlib.image.AxesImage at 0x7fc14fb684a8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [67]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[67]:
<matplotlib.image.AxesImage at 0x7fc1356f63c8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [68]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [69]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_size = (image_width, image_height, image_channels)
    inputs_real = tf.placeholder(tf.float32, (None,*real_size), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate_placeholder = tf.placeholder(tf.float32, None, name='learning_rate')
    
    return inputs_real, inputs_z, learning_rate_placeholder


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [70]:
def leaky_relu(data, alpha=0.01):
     return tf.maximum(alpha*data, data)
In [71]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    keep_probability = 0.8 # for dropout
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # input dim is 28 x 28 x num channels (1 for mnist, 3 for faces)
        x1 = tf.layers.conv2d(images,64,5,strides=2, padding='same')
        relu1 = leaky_relu(x1)
        # 14x14x64 
        
        # (after 1st review to try to improve performance) - using dropout with 50% 
        # after batch normalization
        # ref: "CDk denotes a a Convolution-BatchNormDropout-ReLU layer with a dropout rate of 50%"
        # from paper: "Image-to-Image translation with Conditional Adversarial Networks"
        # (guesstimate: skipping dropout in first layer, assuming it might have somewhat similar 
        #  negative effects as batch_normalization() in first layer for gans?)
        # found the paper through https://github.com/soumith/ganhacks
        
        # (after 1st review input) adding additional layer to improve performance.
        # keep same dimensions - strides=1 and padding is same gives zero padding 
        # and keeps dimensions.
        # useful resource: http://cs231n.github.io/convolutional-networks/#layerpat
        x1b = tf.layers.conv2d(relu1,64,5,strides=1, padding='same')
        bn1b = tf.layers.batch_normalization(x1b,training=True)
        dropout1b = tf.nn.dropout(bn1b, keep_probability)
        relu1b = leaky_relu(dropout1b)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1b, 2*64, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        dropout2 = tf.nn.dropout(bn2, keep_probability)
        relu2 = leaky_relu(dropout2)
        # 7x7x128
        
        # same dimension as previous layer, stride=1 (after review input)
        x2b = tf.layers.conv2d(relu2, 2*64, 5, strides=1, padding='same')
        bn2b = tf.layers.batch_normalization(x2b, training=True)
        dropout2b = tf.nn.dropout(bn2b, keep_probability)
        relu2b = leaky_relu(dropout2b)
        # 7x7x128
        
        # flatten
        flat = tf.reshape(relu2b, (-1,7*7*128))
        
        # TODO: Could perhaps try out 1x1 convolutions instead of fully connected (dense) here
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    

    return (out, logits)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [72]:
def generator(z, out_channel_dim,is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    keep_probability=0.5 # after 1st review, adding dropout, see discriminator
    
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        dense1 = tf.layers.dense(z, 7*7*512)
        reshaped1 = tf.reshape(dense1, (-1,7,7,512))
        bn1 = tf.layers.batch_normalization(reshaped1, training=is_train) 
        relu1 = leaky_relu(bn1)
        # 7x7x512
        
        # after 1st review, strides=1 to keep shape and get more params to train on to
        # perhaps improve generation
        conv1b = tf.layers.conv2d_transpose(relu1, 512, 5, strides=1,padding='same')
        bn1b = tf.layers.batch_normalization(conv1b, training=is_train)
        dropout1b = tf.nn.dropout(bn1b, keep_probability)
        relu1b = leaky_relu(dropout1b)
        # 7x7x512
        
        conv2 = tf.layers.conv2d_transpose(relu1b, 256, 5, strides=2,padding='same')
        bn2 = tf.layers.batch_normalization(conv2, training=is_train)
        dropout2 = tf.nn.dropout(bn2, keep_probability)
        relu2 = leaky_relu(dropout2)
        # 14x14x256
        
        # (after 1st review input) - additional layer to get better generator performance
        # keep dimensions, with strides=1 and padding = 'same' (zero padding)
        # (could perhaps also look into 1x1 convolutions)
        conv2b = tf.layers.conv2d_transpose(relu2, 256, 5, strides=1, padding='same')
        bn2b = tf.layers.batch_normalization(conv2b, training=is_train)
        dropout2b = tf.nn.dropout(bn2b,keep_probability)
        relu2b = leaky_relu(dropout2b)
                                            
        # out_channel_dim is 1 for mnist and 3 for faces, i.e. color dim
        logits = tf.layers.conv2d_transpose(relu2, out_channel_dim,5, 
                                            strides=2,padding='same' )
        
        
        # 28x28x output_dim
        out = tf.tanh(logits)
                
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [73]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    # after 1st review, trying to improve generation with 1-sided smoothing
    # from paper "Improved Techniques for training GANs" (Salimans 2016)
    
    smoothing = 0.1
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
                                               labels = tf.ones_like(d_model_real)*(1-smoothing)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                               labels = tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
                                               labels = tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [74]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS,scope='discriminator')):
        d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
       
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')):
        g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [75]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [76]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, 
          get_batches, data_shape, data_image_mode, show_every=100, 
          print_every=10, n_images=16):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    
    print(z_dim)
    print(data_shape)
    (num_images, image_width, image_height, image_channels) = data_shape
    
    input_real, input_z, learning_rate_placeholder = model_inputs(image_width, image_height, image_channels, z_dim)
    
    print("TYPE input_z:", type(input_z))
    # out_channel_dim = image_channels
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    
    # TODO: learning_rate init? 
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    saver = tf.train.Saver() # TODO: perhaps only generator variables 
    samples, losses = [], []
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):      
                steps += 1
                # TODO: Train Model
                #print("b", batch_images.shape)
                
                # input data batches has values from -0.5 to 0.5 => scale to -1 to 1
                batch_images *= 2.0
                
                # sample random noise for G
                batch_z = np.random.uniform(-1,1,size=(batch_size, z_dim))
            
                # run discriminator and generator optimizers
                _ = sess.run(d_opt, feed_dict={input_real:batch_images,
                                              input_z:batch_z,
                                              learning_rate_placeholder:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real:batch_images,
                                              input_z:batch_z,
                                              learning_rate_placeholder:learning_rate})
                
                if steps % print_every == 0:
                    # get losses and print them out
                    train_loss_d = sess.run(d_loss, {input_real:batch_images,
                                              input_z:batch_z,
                                              learning_rate_placeholder:learning_rate})
                    train_loss_g = g_loss.eval({input_real:batch_images,
                                              input_z:batch_z,
                                              learning_rate_placeholder:learning_rate})
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))    
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g)) 
            
                if steps % show_every  == 0:
                    print(type(batch_z))
                    show_generator_output(sess, n_images=n_images,
                                         input_z=input_z, out_channel_dim=image_channels,
                                         image_mode=data_image_mode)

    # save training generator samples
    with open('train_samples_pkl','wb') as fh:
        import pickle as pkl
        pkl.dump(samples,fh)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [78]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
100
(60000, 28, 28, 1)
TYPE input_z: <class 'tensorflow.python.framework.ops.Tensor'>
Epoch 1/10... Discriminator Loss: 1.7857... Generator Loss: 0.4502
Epoch 1/10... Discriminator Loss: 0.9644... Generator Loss: 1.4778
Epoch 1/10... Discriminator Loss: 0.7873... Generator Loss: 1.4719
Epoch 1/10... Discriminator Loss: 0.4631... Generator Loss: 4.0671
Epoch 1/10... Discriminator Loss: 0.4735... Generator Loss: 3.1534
Epoch 1/10... Discriminator Loss: 0.7241... Generator Loss: 1.8552
Epoch 1/10... Discriminator Loss: 0.4386... Generator Loss: 3.6769
Epoch 1/10... Discriminator Loss: 0.5425... Generator Loss: 2.9746
Epoch 1/10... Discriminator Loss: 0.4643... Generator Loss: 4.4702
Epoch 1/10... Discriminator Loss: 0.4124... Generator Loss: 4.1970
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.4269... Generator Loss: 4.0783
Epoch 1/10... Discriminator Loss: 0.4109... Generator Loss: 4.1745
Epoch 1/10... Discriminator Loss: 0.7084... Generator Loss: 3.5350
Epoch 1/10... Discriminator Loss: 0.4775... Generator Loss: 3.3955
Epoch 1/10... Discriminator Loss: 0.4628... Generator Loss: 3.1426
Epoch 1/10... Discriminator Loss: 1.2038... Generator Loss: 0.8935
Epoch 1/10... Discriminator Loss: 0.8792... Generator Loss: 1.5462
Epoch 1/10... Discriminator Loss: 0.7932... Generator Loss: 2.8421
Epoch 1/10... Discriminator Loss: 0.7636... Generator Loss: 2.2550
Epoch 1/10... Discriminator Loss: 1.0260... Generator Loss: 1.2591
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.7202... Generator Loss: 4.5735
Epoch 1/10... Discriminator Loss: 0.9133... Generator Loss: 4.3565
Epoch 1/10... Discriminator Loss: 1.4294... Generator Loss: 5.3426
Epoch 1/10... Discriminator Loss: 0.7976... Generator Loss: 1.8544
Epoch 1/10... Discriminator Loss: 0.7345... Generator Loss: 1.8437
Epoch 1/10... Discriminator Loss: 0.8898... Generator Loss: 1.5871
Epoch 1/10... Discriminator Loss: 0.8486... Generator Loss: 1.6676
Epoch 1/10... Discriminator Loss: 0.8012... Generator Loss: 2.0166
Epoch 1/10... Discriminator Loss: 0.9473... Generator Loss: 1.3511
Epoch 1/10... Discriminator Loss: 0.8993... Generator Loss: 2.6258
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.7702... Generator Loss: 2.1085
Epoch 1/10... Discriminator Loss: 0.9374... Generator Loss: 2.1491
Epoch 1/10... Discriminator Loss: 1.0094... Generator Loss: 2.1374
Epoch 1/10... Discriminator Loss: 0.8995... Generator Loss: 1.6783
Epoch 1/10... Discriminator Loss: 1.2125... Generator Loss: 1.0667
Epoch 1/10... Discriminator Loss: 1.0375... Generator Loss: 1.6843
Epoch 1/10... Discriminator Loss: 1.1023... Generator Loss: 1.7753
Epoch 1/10... Discriminator Loss: 1.1299... Generator Loss: 1.2673
Epoch 1/10... Discriminator Loss: 1.4135... Generator Loss: 0.7583
Epoch 1/10... Discriminator Loss: 1.0558... Generator Loss: 1.4366
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.9827... Generator Loss: 1.7256
Epoch 1/10... Discriminator Loss: 1.2550... Generator Loss: 0.9043
Epoch 1/10... Discriminator Loss: 1.0247... Generator Loss: 1.2223
Epoch 1/10... Discriminator Loss: 1.1106... Generator Loss: 2.1252
Epoch 1/10... Discriminator Loss: 1.0787... Generator Loss: 0.9904
Epoch 1/10... Discriminator Loss: 1.0703... Generator Loss: 1.3071
Epoch 2/10... Discriminator Loss: 0.9537... Generator Loss: 2.0911
Epoch 2/10... Discriminator Loss: 1.4266... Generator Loss: 2.5404
Epoch 2/10... Discriminator Loss: 1.0133... Generator Loss: 1.4595
Epoch 2/10... Discriminator Loss: 0.9524... Generator Loss: 1.6762
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0628... Generator Loss: 2.3487
Epoch 2/10... Discriminator Loss: 1.1100... Generator Loss: 1.1467
Epoch 2/10... Discriminator Loss: 1.2276... Generator Loss: 2.1780
Epoch 2/10... Discriminator Loss: 1.0786... Generator Loss: 1.0445
Epoch 2/10... Discriminator Loss: 1.0664... Generator Loss: 1.9545
Epoch 2/10... Discriminator Loss: 1.2904... Generator Loss: 0.7796
Epoch 2/10... Discriminator Loss: 1.0498... Generator Loss: 1.1563
Epoch 2/10... Discriminator Loss: 1.3568... Generator Loss: 0.6990
Epoch 2/10... Discriminator Loss: 0.9691... Generator Loss: 1.0725
Epoch 2/10... Discriminator Loss: 1.2304... Generator Loss: 2.5989
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 0.9914... Generator Loss: 1.3369
Epoch 2/10... Discriminator Loss: 1.1685... Generator Loss: 0.9213
Epoch 2/10... Discriminator Loss: 1.1624... Generator Loss: 2.3909
Epoch 2/10... Discriminator Loss: 0.9756... Generator Loss: 1.5997
Epoch 2/10... Discriminator Loss: 1.0274... Generator Loss: 1.0354
Epoch 2/10... Discriminator Loss: 1.0297... Generator Loss: 1.9508
Epoch 2/10... Discriminator Loss: 0.9894... Generator Loss: 1.8083
Epoch 2/10... Discriminator Loss: 1.0482... Generator Loss: 1.1597
Epoch 2/10... Discriminator Loss: 0.9517... Generator Loss: 1.3205
Epoch 2/10... Discriminator Loss: 0.9563... Generator Loss: 1.0488
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0110... Generator Loss: 1.6426
Epoch 2/10... Discriminator Loss: 0.8786... Generator Loss: 1.5022
Epoch 2/10... Discriminator Loss: 1.0147... Generator Loss: 2.1148
Epoch 2/10... Discriminator Loss: 1.0884... Generator Loss: 0.9974
Epoch 2/10... Discriminator Loss: 0.9927... Generator Loss: 1.2056
Epoch 2/10... Discriminator Loss: 0.9719... Generator Loss: 1.1450
Epoch 2/10... Discriminator Loss: 0.9448... Generator Loss: 1.1613
Epoch 2/10... Discriminator Loss: 1.2748... Generator Loss: 0.7282
Epoch 2/10... Discriminator Loss: 0.9113... Generator Loss: 1.7644
Epoch 2/10... Discriminator Loss: 0.8627... Generator Loss: 1.3516
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0461... Generator Loss: 1.0145
Epoch 2/10... Discriminator Loss: 1.0025... Generator Loss: 1.1247
Epoch 2/10... Discriminator Loss: 0.8699... Generator Loss: 1.2452
Epoch 2/10... Discriminator Loss: 0.9420... Generator Loss: 1.5132
Epoch 2/10... Discriminator Loss: 0.9082... Generator Loss: 1.4605
Epoch 2/10... Discriminator Loss: 0.8991... Generator Loss: 2.2923
Epoch 2/10... Discriminator Loss: 0.8202... Generator Loss: 1.5177
Epoch 2/10... Discriminator Loss: 1.5546... Generator Loss: 3.2802
Epoch 2/10... Discriminator Loss: 0.9827... Generator Loss: 1.2030
Epoch 2/10... Discriminator Loss: 0.8517... Generator Loss: 1.8538
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 0.9919... Generator Loss: 1.1900
Epoch 2/10... Discriminator Loss: 0.8737... Generator Loss: 1.3245
Epoch 2/10... Discriminator Loss: 0.8434... Generator Loss: 2.0557
Epoch 3/10... Discriminator Loss: 0.8978... Generator Loss: 1.7363
Epoch 3/10... Discriminator Loss: 0.9763... Generator Loss: 2.3827
Epoch 3/10... Discriminator Loss: 0.8479... Generator Loss: 1.7689
Epoch 3/10... Discriminator Loss: 0.8549... Generator Loss: 1.7116
Epoch 3/10... Discriminator Loss: 1.0537... Generator Loss: 2.8533
Epoch 3/10... Discriminator Loss: 1.0755... Generator Loss: 2.5206
Epoch 3/10... Discriminator Loss: 0.8189... Generator Loss: 1.9394
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8443... Generator Loss: 1.5352
Epoch 3/10... Discriminator Loss: 0.9050... Generator Loss: 1.8461
Epoch 3/10... Discriminator Loss: 1.3518... Generator Loss: 0.5894
Epoch 3/10... Discriminator Loss: 1.0382... Generator Loss: 1.1260
Epoch 3/10... Discriminator Loss: 0.7987... Generator Loss: 1.6448
Epoch 3/10... Discriminator Loss: 0.9211... Generator Loss: 2.0654
Epoch 3/10... Discriminator Loss: 0.8722... Generator Loss: 2.1494
Epoch 3/10... Discriminator Loss: 0.7943... Generator Loss: 2.0200
Epoch 3/10... Discriminator Loss: 1.2173... Generator Loss: 0.7977
Epoch 3/10... Discriminator Loss: 0.7827... Generator Loss: 1.4296
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.7677... Generator Loss: 1.5801
Epoch 3/10... Discriminator Loss: 0.9680... Generator Loss: 2.4600
Epoch 3/10... Discriminator Loss: 0.8474... Generator Loss: 1.4909
Epoch 3/10... Discriminator Loss: 0.8364... Generator Loss: 1.4597
Epoch 3/10... Discriminator Loss: 0.8031... Generator Loss: 1.5726
Epoch 3/10... Discriminator Loss: 0.9109... Generator Loss: 1.4504
Epoch 3/10... Discriminator Loss: 0.8113... Generator Loss: 1.5011
Epoch 3/10... Discriminator Loss: 0.6945... Generator Loss: 1.9220
Epoch 3/10... Discriminator Loss: 0.8737... Generator Loss: 2.3812
Epoch 3/10... Discriminator Loss: 0.7386... Generator Loss: 1.8167
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.7727... Generator Loss: 2.5824
Epoch 3/10... Discriminator Loss: 1.0861... Generator Loss: 1.0296
Epoch 3/10... Discriminator Loss: 0.8368... Generator Loss: 1.4569
Epoch 3/10... Discriminator Loss: 0.8782... Generator Loss: 1.3561
Epoch 3/10... Discriminator Loss: 0.7125... Generator Loss: 2.0271
Epoch 3/10... Discriminator Loss: 0.6887... Generator Loss: 2.2688
Epoch 3/10... Discriminator Loss: 0.7843... Generator Loss: 1.4917
Epoch 3/10... Discriminator Loss: 1.0534... Generator Loss: 1.9349
Epoch 3/10... Discriminator Loss: 0.7249... Generator Loss: 1.9226
Epoch 3/10... Discriminator Loss: 0.7595... Generator Loss: 1.8335
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8834... Generator Loss: 1.4816
Epoch 3/10... Discriminator Loss: 0.7071... Generator Loss: 1.5830
Epoch 3/10... Discriminator Loss: 0.8121... Generator Loss: 1.4901
Epoch 3/10... Discriminator Loss: 0.8021... Generator Loss: 1.4879
Epoch 3/10... Discriminator Loss: 1.4575... Generator Loss: 3.7286
Epoch 3/10... Discriminator Loss: 0.9187... Generator Loss: 1.3201
Epoch 3/10... Discriminator Loss: 0.7381... Generator Loss: 1.8723
Epoch 3/10... Discriminator Loss: 0.7254... Generator Loss: 1.9411
Epoch 3/10... Discriminator Loss: 0.7598... Generator Loss: 2.5848
Epoch 3/10... Discriminator Loss: 0.6208... Generator Loss: 2.0574
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.4426... Generator Loss: 0.5693
Epoch 4/10... Discriminator Loss: 0.6531... Generator Loss: 2.4106
Epoch 4/10... Discriminator Loss: 0.7059... Generator Loss: 1.8651
Epoch 4/10... Discriminator Loss: 0.6426... Generator Loss: 1.5393
Epoch 4/10... Discriminator Loss: 2.6885... Generator Loss: 4.6151
Epoch 4/10... Discriminator Loss: 0.8971... Generator Loss: 1.6635
Epoch 4/10... Discriminator Loss: 0.7858... Generator Loss: 2.1746
Epoch 4/10... Discriminator Loss: 0.7523... Generator Loss: 1.3337
Epoch 4/10... Discriminator Loss: 0.7271... Generator Loss: 1.8644
Epoch 4/10... Discriminator Loss: 0.6313... Generator Loss: 2.4873
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.6004... Generator Loss: 2.3379
Epoch 4/10... Discriminator Loss: 0.6650... Generator Loss: 2.3039
Epoch 4/10... Discriminator Loss: 0.6512... Generator Loss: 2.4034
Epoch 4/10... Discriminator Loss: 1.2123... Generator Loss: 3.0590
Epoch 4/10... Discriminator Loss: 0.7548... Generator Loss: 1.7841
Epoch 4/10... Discriminator Loss: 0.5832... Generator Loss: 2.1196
Epoch 4/10... Discriminator Loss: 0.7485... Generator Loss: 1.6994
Epoch 4/10... Discriminator Loss: 0.8029... Generator Loss: 2.7781
Epoch 4/10... Discriminator Loss: 0.5821... Generator Loss: 1.9816
Epoch 4/10... Discriminator Loss: 0.6888... Generator Loss: 1.6655
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.6748... Generator Loss: 1.5574
Epoch 4/10... Discriminator Loss: 0.6755... Generator Loss: 1.8607
Epoch 4/10... Discriminator Loss: 0.9808... Generator Loss: 1.0409
Epoch 4/10... Discriminator Loss: 0.8201... Generator Loss: 1.5167
Epoch 4/10... Discriminator Loss: 0.6748... Generator Loss: 2.5508
Epoch 4/10... Discriminator Loss: 0.5958... Generator Loss: 2.4094
Epoch 4/10... Discriminator Loss: 0.6376... Generator Loss: 2.8105
Epoch 4/10... Discriminator Loss: 0.5827... Generator Loss: 2.6347
Epoch 4/10... Discriminator Loss: 0.5386... Generator Loss: 2.4036
Epoch 4/10... Discriminator Loss: 1.1071... Generator Loss: 1.7607
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.6635... Generator Loss: 1.8316
Epoch 4/10... Discriminator Loss: 0.5937... Generator Loss: 3.3338
Epoch 4/10... Discriminator Loss: 0.5992... Generator Loss: 2.6529
Epoch 4/10... Discriminator Loss: 0.6081... Generator Loss: 2.3409
Epoch 4/10... Discriminator Loss: 0.6339... Generator Loss: 3.0011
Epoch 4/10... Discriminator Loss: 0.8694... Generator Loss: 2.7654
Epoch 4/10... Discriminator Loss: 0.5273... Generator Loss: 2.3395
Epoch 4/10... Discriminator Loss: 0.5901... Generator Loss: 2.9449
Epoch 4/10... Discriminator Loss: 0.5821... Generator Loss: 2.5905
Epoch 4/10... Discriminator Loss: 0.6847... Generator Loss: 1.6490
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.5331... Generator Loss: 2.3994
Epoch 4/10... Discriminator Loss: 2.9110... Generator Loss: 5.5421
Epoch 4/10... Discriminator Loss: 0.8482... Generator Loss: 1.4156
Epoch 4/10... Discriminator Loss: 0.6505... Generator Loss: 2.9814
Epoch 4/10... Discriminator Loss: 0.6236... Generator Loss: 2.3673
Epoch 4/10... Discriminator Loss: 0.5919... Generator Loss: 2.4253
Epoch 4/10... Discriminator Loss: 0.6924... Generator Loss: 1.6887
Epoch 5/10... Discriminator Loss: 0.5892... Generator Loss: 2.0097
Epoch 5/10... Discriminator Loss: 0.6014... Generator Loss: 2.1924
Epoch 5/10... Discriminator Loss: 2.0111... Generator Loss: 4.8386
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 1.1590... Generator Loss: 0.8845
Epoch 5/10... Discriminator Loss: 0.5890... Generator Loss: 1.9165
Epoch 5/10... Discriminator Loss: 0.5456... Generator Loss: 2.2839
Epoch 5/10... Discriminator Loss: 0.5644... Generator Loss: 2.5770
Epoch 5/10... Discriminator Loss: 0.7042... Generator Loss: 1.4857
Epoch 5/10... Discriminator Loss: 0.6249... Generator Loss: 2.1134
Epoch 5/10... Discriminator Loss: 0.6308... Generator Loss: 2.0031
Epoch 5/10... Discriminator Loss: 0.6897... Generator Loss: 4.1597
Epoch 5/10... Discriminator Loss: 0.5825... Generator Loss: 3.3269
Epoch 5/10... Discriminator Loss: 0.6853... Generator Loss: 3.5465
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.5017... Generator Loss: 2.5265
Epoch 5/10... Discriminator Loss: 0.5930... Generator Loss: 2.2981
Epoch 5/10... Discriminator Loss: 0.5656... Generator Loss: 2.2333
Epoch 5/10... Discriminator Loss: 0.5121... Generator Loss: 2.4055
Epoch 5/10... Discriminator Loss: 0.5791... Generator Loss: 3.1373
Epoch 5/10... Discriminator Loss: 0.5056... Generator Loss: 2.8872
Epoch 5/10... Discriminator Loss: 0.5126... Generator Loss: 2.4136
Epoch 5/10... Discriminator Loss: 0.5342... Generator Loss: 3.0466
Epoch 5/10... Discriminator Loss: 0.5196... Generator Loss: 2.6062
Epoch 5/10... Discriminator Loss: 0.7127... Generator Loss: 1.6017
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.4574... Generator Loss: 3.1521
Epoch 5/10... Discriminator Loss: 2.7442... Generator Loss: 0.2110
Epoch 5/10... Discriminator Loss: 0.6132... Generator Loss: 2.3172
Epoch 5/10... Discriminator Loss: 0.5651... Generator Loss: 2.7184
Epoch 5/10... Discriminator Loss: 0.5455... Generator Loss: 2.5662
Epoch 5/10... Discriminator Loss: 0.4929... Generator Loss: 3.1011
Epoch 5/10... Discriminator Loss: 0.5088... Generator Loss: 2.9460
Epoch 5/10... Discriminator Loss: 0.6725... Generator Loss: 1.9570
Epoch 5/10... Discriminator Loss: 0.6205... Generator Loss: 2.1781
Epoch 5/10... Discriminator Loss: 0.5493... Generator Loss: 2.2103
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.5479... Generator Loss: 3.5669
Epoch 5/10... Discriminator Loss: 0.5076... Generator Loss: 2.7035
Epoch 5/10... Discriminator Loss: 0.4752... Generator Loss: 2.9274
Epoch 5/10... Discriminator Loss: 0.4819... Generator Loss: 2.6243
Epoch 5/10... Discriminator Loss: 0.5408... Generator Loss: 2.8112
Epoch 5/10... Discriminator Loss: 0.6742... Generator Loss: 1.6788
Epoch 5/10... Discriminator Loss: 0.5141... Generator Loss: 3.1502
Epoch 5/10... Discriminator Loss: 0.5274... Generator Loss: 2.3671
Epoch 5/10... Discriminator Loss: 0.5255... Generator Loss: 2.5218
Epoch 5/10... Discriminator Loss: 0.5232... Generator Loss: 2.7628
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.5758... Generator Loss: 2.9520
Epoch 5/10... Discriminator Loss: 0.5096... Generator Loss: 2.7036
Epoch 5/10... Discriminator Loss: 0.7433... Generator Loss: 1.3659
Epoch 5/10... Discriminator Loss: 0.8832... Generator Loss: 2.1970
Epoch 6/10... Discriminator Loss: 0.5926... Generator Loss: 2.2662
Epoch 6/10... Discriminator Loss: 0.4768... Generator Loss: 2.5523
Epoch 6/10... Discriminator Loss: 0.4820... Generator Loss: 2.9933
Epoch 6/10... Discriminator Loss: 0.5080... Generator Loss: 3.2017
Epoch 6/10... Discriminator Loss: 0.5517... Generator Loss: 4.4439
Epoch 6/10... Discriminator Loss: 0.6020... Generator Loss: 2.0361
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.5322... Generator Loss: 2.3310
Epoch 6/10... Discriminator Loss: 0.4411... Generator Loss: 3.1679
Epoch 6/10... Discriminator Loss: 0.4746... Generator Loss: 3.0870
Epoch 6/10... Discriminator Loss: 0.4512... Generator Loss: 3.7568
Epoch 6/10... Discriminator Loss: 0.6374... Generator Loss: 3.7411
Epoch 6/10... Discriminator Loss: 0.6300... Generator Loss: 3.5570
Epoch 6/10... Discriminator Loss: 0.5121... Generator Loss: 2.7307
Epoch 6/10... Discriminator Loss: 0.4681... Generator Loss: 2.9418
Epoch 6/10... Discriminator Loss: 0.4478... Generator Loss: 3.1446
Epoch 6/10... Discriminator Loss: 0.8867... Generator Loss: 1.9875
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.5794... Generator Loss: 2.8722
Epoch 6/10... Discriminator Loss: 0.5345... Generator Loss: 2.1063
Epoch 6/10... Discriminator Loss: 0.4498... Generator Loss: 3.5491
Epoch 6/10... Discriminator Loss: 0.9004... Generator Loss: 3.3889
Epoch 6/10... Discriminator Loss: 0.5892... Generator Loss: 2.1337
Epoch 6/10... Discriminator Loss: 0.5213... Generator Loss: 2.4444
Epoch 6/10... Discriminator Loss: 0.4509... Generator Loss: 3.2834
Epoch 6/10... Discriminator Loss: 0.4664... Generator Loss: 2.9754
Epoch 6/10... Discriminator Loss: 0.5794... Generator Loss: 1.7915
Epoch 6/10... Discriminator Loss: 0.4595... Generator Loss: 2.9780
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 1.4207... Generator Loss: 5.2729
Epoch 6/10... Discriminator Loss: 0.6592... Generator Loss: 2.1058
Epoch 6/10... Discriminator Loss: 0.5062... Generator Loss: 2.8977
Epoch 6/10... Discriminator Loss: 0.5556... Generator Loss: 2.2420
Epoch 6/10... Discriminator Loss: 0.4503... Generator Loss: 3.3092
Epoch 6/10... Discriminator Loss: 0.4468... Generator Loss: 3.2609
Epoch 6/10... Discriminator Loss: 0.7068... Generator Loss: 1.7036
Epoch 6/10... Discriminator Loss: 0.5074... Generator Loss: 4.1297
Epoch 6/10... Discriminator Loss: 0.5211... Generator Loss: 2.8984
Epoch 6/10... Discriminator Loss: 0.4521... Generator Loss: 2.7994
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.4842... Generator Loss: 3.1725
Epoch 6/10... Discriminator Loss: 0.7295... Generator Loss: 5.0774
Epoch 6/10... Discriminator Loss: 0.4562... Generator Loss: 2.8420
Epoch 6/10... Discriminator Loss: 0.4800... Generator Loss: 3.3095
Epoch 6/10... Discriminator Loss: 0.9256... Generator Loss: 1.7063
Epoch 6/10... Discriminator Loss: 0.6259... Generator Loss: 1.9669
Epoch 6/10... Discriminator Loss: 0.4785... Generator Loss: 3.1954
Epoch 6/10... Discriminator Loss: 0.4480... Generator Loss: 3.1603
Epoch 6/10... Discriminator Loss: 0.4430... Generator Loss: 2.8817
Epoch 6/10... Discriminator Loss: 0.4621... Generator Loss: 2.9203
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.4584... Generator Loss: 3.8957
Epoch 7/10... Discriminator Loss: 0.4401... Generator Loss: 3.4280
Epoch 7/10... Discriminator Loss: 1.0899... Generator Loss: 1.0730
Epoch 7/10... Discriminator Loss: 0.7705... Generator Loss: 1.5056
Epoch 7/10... Discriminator Loss: 0.4716... Generator Loss: 3.6803
Epoch 7/10... Discriminator Loss: 0.5482... Generator Loss: 2.2761
Epoch 7/10... Discriminator Loss: 0.4727... Generator Loss: 3.6655
Epoch 7/10... Discriminator Loss: 0.4483... Generator Loss: 3.5286
Epoch 7/10... Discriminator Loss: 0.4287... Generator Loss: 3.2520
Epoch 7/10... Discriminator Loss: 0.7362... Generator Loss: 1.1752
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.5311... Generator Loss: 2.5450
Epoch 7/10... Discriminator Loss: 0.4574... Generator Loss: 3.1100
Epoch 7/10... Discriminator Loss: 0.4155... Generator Loss: 3.3854
Epoch 7/10... Discriminator Loss: 0.4354... Generator Loss: 2.9987
Epoch 7/10... Discriminator Loss: 0.4301... Generator Loss: 3.1895
Epoch 7/10... Discriminator Loss: 0.4586... Generator Loss: 3.1458
Epoch 7/10... Discriminator Loss: 0.4527... Generator Loss: 3.8506
Epoch 7/10... Discriminator Loss: 0.5768... Generator Loss: 2.1506
Epoch 7/10... Discriminator Loss: 0.4575... Generator Loss: 3.0982
Epoch 7/10... Discriminator Loss: 0.4576... Generator Loss: 3.8341
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.6502... Generator Loss: 5.4531
Epoch 7/10... Discriminator Loss: 0.5237... Generator Loss: 2.5373
Epoch 7/10... Discriminator Loss: 0.4646... Generator Loss: 4.2551
Epoch 7/10... Discriminator Loss: 0.4309... Generator Loss: 3.7147
Epoch 7/10... Discriminator Loss: 0.4297... Generator Loss: 4.2414
Epoch 7/10... Discriminator Loss: 0.8786... Generator Loss: 2.3749
Epoch 7/10... Discriminator Loss: 0.5834... Generator Loss: 2.6267
Epoch 7/10... Discriminator Loss: 0.5517... Generator Loss: 2.2785
Epoch 7/10... Discriminator Loss: 0.5064... Generator Loss: 2.4730
Epoch 7/10... Discriminator Loss: 0.8093... Generator Loss: 1.5656
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.4418... Generator Loss: 3.3787
Epoch 7/10... Discriminator Loss: 0.4543... Generator Loss: 3.6479
Epoch 7/10... Discriminator Loss: 0.4472... Generator Loss: 3.1409
Epoch 7/10... Discriminator Loss: 0.5392... Generator Loss: 2.4642
Epoch 7/10... Discriminator Loss: 0.4965... Generator Loss: 2.6673
Epoch 7/10... Discriminator Loss: 0.4123... Generator Loss: 3.4146
Epoch 7/10... Discriminator Loss: 0.4208... Generator Loss: 3.9472
Epoch 7/10... Discriminator Loss: 0.4096... Generator Loss: 3.3839
Epoch 7/10... Discriminator Loss: 0.5172... Generator Loss: 2.4463
Epoch 7/10... Discriminator Loss: 0.4450... Generator Loss: 3.3667
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.3986... Generator Loss: 4.1467
Epoch 7/10... Discriminator Loss: 0.5256... Generator Loss: 5.0294
Epoch 7/10... Discriminator Loss: 0.4139... Generator Loss: 5.1984
Epoch 7/10... Discriminator Loss: 0.4404... Generator Loss: 3.0081
Epoch 7/10... Discriminator Loss: 4.4224... Generator Loss: 6.4505
Epoch 7/10... Discriminator Loss: 0.5711... Generator Loss: 2.6145
Epoch 7/10... Discriminator Loss: 0.4495... Generator Loss: 2.9012
Epoch 8/10... Discriminator Loss: 0.4747... Generator Loss: 3.9373
Epoch 8/10... Discriminator Loss: 0.4272... Generator Loss: 3.6538
Epoch 8/10... Discriminator Loss: 0.4306... Generator Loss: 3.5757
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4685... Generator Loss: 3.5341
Epoch 8/10... Discriminator Loss: 0.4480... Generator Loss: 4.0503
Epoch 8/10... Discriminator Loss: 0.4173... Generator Loss: 4.0670
Epoch 8/10... Discriminator Loss: 0.4106... Generator Loss: 3.3231
Epoch 8/10... Discriminator Loss: 0.4385... Generator Loss: 3.8276
Epoch 8/10... Discriminator Loss: 0.4179... Generator Loss: 3.6972
Epoch 8/10... Discriminator Loss: 1.2137... Generator Loss: 1.9281
Epoch 8/10... Discriminator Loss: 0.7359... Generator Loss: 1.9123
Epoch 8/10... Discriminator Loss: 0.5536... Generator Loss: 3.6465
Epoch 8/10... Discriminator Loss: 0.5074... Generator Loss: 3.7870
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.5057... Generator Loss: 4.3318
Epoch 8/10... Discriminator Loss: 0.4629... Generator Loss: 3.3795
Epoch 8/10... Discriminator Loss: 0.4646... Generator Loss: 3.1941
Epoch 8/10... Discriminator Loss: 0.4246... Generator Loss: 3.4595
Epoch 8/10... Discriminator Loss: 0.4194... Generator Loss: 4.2384
Epoch 8/10... Discriminator Loss: 0.7756... Generator Loss: 4.1714
Epoch 8/10... Discriminator Loss: 0.4814... Generator Loss: 2.7620
Epoch 8/10... Discriminator Loss: 0.4834... Generator Loss: 2.3898
Epoch 8/10... Discriminator Loss: 0.4588... Generator Loss: 3.6921
Epoch 8/10... Discriminator Loss: 0.4407... Generator Loss: 3.0059
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4184... Generator Loss: 3.3702
Epoch 8/10... Discriminator Loss: 0.4089... Generator Loss: 5.0815
Epoch 8/10... Discriminator Loss: 0.3987... Generator Loss: 3.9475
Epoch 8/10... Discriminator Loss: 0.4068... Generator Loss: 3.6055
Epoch 8/10... Discriminator Loss: 0.4248... Generator Loss: 3.5863
Epoch 8/10... Discriminator Loss: 0.4363... Generator Loss: 3.7199
Epoch 8/10... Discriminator Loss: 0.4672... Generator Loss: 2.9235
Epoch 8/10... Discriminator Loss: 0.5011... Generator Loss: 2.2668
Epoch 8/10... Discriminator Loss: 0.4407... Generator Loss: 4.0035
Epoch 8/10... Discriminator Loss: 0.4256... Generator Loss: 3.8645
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4063... Generator Loss: 4.0839
Epoch 8/10... Discriminator Loss: 0.4501... Generator Loss: 5.3963
Epoch 8/10... Discriminator Loss: 0.3893... Generator Loss: 4.0108
Epoch 8/10... Discriminator Loss: 0.4382... Generator Loss: 3.4366
Epoch 8/10... Discriminator Loss: 0.3922... Generator Loss: 4.1251
Epoch 8/10... Discriminator Loss: 0.5977... Generator Loss: 4.8820
Epoch 8/10... Discriminator Loss: 0.5076... Generator Loss: 3.3420
Epoch 8/10... Discriminator Loss: 0.4493... Generator Loss: 3.1299
Epoch 8/10... Discriminator Loss: 0.5757... Generator Loss: 1.9851
Epoch 8/10... Discriminator Loss: 0.3984... Generator Loss: 3.3745
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4273... Generator Loss: 4.9922
Epoch 8/10... Discriminator Loss: 0.4103... Generator Loss: 3.9016
Epoch 8/10... Discriminator Loss: 0.6229... Generator Loss: 2.1465
Epoch 8/10... Discriminator Loss: 0.8089... Generator Loss: 3.8899
Epoch 9/10... Discriminator Loss: 0.4865... Generator Loss: 2.0729
Epoch 9/10... Discriminator Loss: 0.4098... Generator Loss: 3.4169
Epoch 9/10... Discriminator Loss: 0.4394... Generator Loss: 3.7284
Epoch 9/10... Discriminator Loss: 0.3921... Generator Loss: 3.8841
Epoch 9/10... Discriminator Loss: 0.5356... Generator Loss: 2.9632
Epoch 9/10... Discriminator Loss: 0.4707... Generator Loss: 2.4618
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.4381... Generator Loss: 3.7338
Epoch 9/10... Discriminator Loss: 0.4433... Generator Loss: 3.3307
Epoch 9/10... Discriminator Loss: 0.5096... Generator Loss: 2.9556
Epoch 9/10... Discriminator Loss: 0.4761... Generator Loss: 2.4561
Epoch 9/10... Discriminator Loss: 0.6974... Generator Loss: 1.9804
Epoch 9/10... Discriminator Loss: 0.4224... Generator Loss: 3.2720
Epoch 9/10... Discriminator Loss: 0.5316... Generator Loss: 2.7233
Epoch 9/10... Discriminator Loss: 0.4213... Generator Loss: 3.1641
Epoch 9/10... Discriminator Loss: 0.4258... Generator Loss: 3.6745
Epoch 9/10... Discriminator Loss: 0.6385... Generator Loss: 1.9915
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.4246... Generator Loss: 3.5730
Epoch 9/10... Discriminator Loss: 2.5184... Generator Loss: 4.7163
Epoch 9/10... Discriminator Loss: 0.5258... Generator Loss: 3.2345
Epoch 9/10... Discriminator Loss: 0.5911... Generator Loss: 2.1919
Epoch 9/10... Discriminator Loss: 0.4289... Generator Loss: 3.8723
Epoch 9/10... Discriminator Loss: 0.4881... Generator Loss: 2.8357
Epoch 9/10... Discriminator Loss: 0.9396... Generator Loss: 1.3184
Epoch 9/10... Discriminator Loss: 0.4071... Generator Loss: 3.9494
Epoch 9/10... Discriminator Loss: 0.4078... Generator Loss: 3.2834
Epoch 9/10... Discriminator Loss: 0.4180... Generator Loss: 3.8344
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.4295... Generator Loss: 4.9645
Epoch 9/10... Discriminator Loss: 0.5402... Generator Loss: 2.6144
Epoch 9/10... Discriminator Loss: 0.3966... Generator Loss: 4.0082
Epoch 9/10... Discriminator Loss: 0.5289... Generator Loss: 2.1801
Epoch 9/10... Discriminator Loss: 0.4029... Generator Loss: 3.3001
Epoch 9/10... Discriminator Loss: 0.4072... Generator Loss: 3.8302
Epoch 9/10... Discriminator Loss: 0.4328... Generator Loss: 3.5350
Epoch 9/10... Discriminator Loss: 0.5800... Generator Loss: 2.2609
Epoch 9/10... Discriminator Loss: 1.0497... Generator Loss: 0.9856
Epoch 9/10... Discriminator Loss: 0.4518... Generator Loss: 3.2676
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.4129... Generator Loss: 4.2612
Epoch 9/10... Discriminator Loss: 0.4003... Generator Loss: 3.8583
Epoch 9/10... Discriminator Loss: 0.4689... Generator Loss: 3.0322
Epoch 9/10... Discriminator Loss: 0.4813... Generator Loss: 2.5855
Epoch 9/10... Discriminator Loss: 0.3954... Generator Loss: 3.7112
Epoch 9/10... Discriminator Loss: 0.4540... Generator Loss: 4.2274
Epoch 9/10... Discriminator Loss: 0.4156... Generator Loss: 3.3506
Epoch 9/10... Discriminator Loss: 0.6480... Generator Loss: 3.4061
Epoch 9/10... Discriminator Loss: 0.3926... Generator Loss: 4.1628
Epoch 9/10... Discriminator Loss: 0.4374... Generator Loss: 3.8418
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.6736... Generator Loss: 1.9280
Epoch 10/10... Discriminator Loss: 0.3990... Generator Loss: 4.4545
Epoch 10/10... Discriminator Loss: 0.4000... Generator Loss: 4.1872
Epoch 10/10... Discriminator Loss: 0.3830... Generator Loss: 5.1093
Epoch 10/10... Discriminator Loss: 1.3875... Generator Loss: 0.9718
Epoch 10/10... Discriminator Loss: 1.2075... Generator Loss: 1.3340
Epoch 10/10... Discriminator Loss: 1.0777... Generator Loss: 2.0191
Epoch 10/10... Discriminator Loss: 0.7662... Generator Loss: 1.3881
Epoch 10/10... Discriminator Loss: 0.5263... Generator Loss: 2.4092
Epoch 10/10... Discriminator Loss: 0.4344... Generator Loss: 2.6736
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.4425... Generator Loss: 3.9955
Epoch 10/10... Discriminator Loss: 0.4199... Generator Loss: 4.0142
Epoch 10/10... Discriminator Loss: 0.3856... Generator Loss: 4.0121
Epoch 10/10... Discriminator Loss: 0.4563... Generator Loss: 3.8572
Epoch 10/10... Discriminator Loss: 0.4168... Generator Loss: 3.1528
Epoch 10/10... Discriminator Loss: 0.5064... Generator Loss: 2.4105
Epoch 10/10... Discriminator Loss: 0.6178... Generator Loss: 1.6474
Epoch 10/10... Discriminator Loss: 0.4141... Generator Loss: 2.5876
Epoch 10/10... Discriminator Loss: 0.7884... Generator Loss: 1.7117
Epoch 10/10... Discriminator Loss: 0.5993... Generator Loss: 2.3561
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.4424... Generator Loss: 3.5413
Epoch 10/10... Discriminator Loss: 0.4323... Generator Loss: 3.3002
Epoch 10/10... Discriminator Loss: 0.4204... Generator Loss: 3.2037
Epoch 10/10... Discriminator Loss: 0.4146... Generator Loss: 4.2876
Epoch 10/10... Discriminator Loss: 0.3965... Generator Loss: 4.2047
Epoch 10/10... Discriminator Loss: 0.3795... Generator Loss: 3.6741
Epoch 10/10... Discriminator Loss: 0.4025... Generator Loss: 2.7975
Epoch 10/10... Discriminator Loss: 0.4241... Generator Loss: 3.4339
Epoch 10/10... Discriminator Loss: 0.4358... Generator Loss: 2.9341
Epoch 10/10... Discriminator Loss: 0.3947... Generator Loss: 3.7470
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 1.7033... Generator Loss: 0.4503
Epoch 10/10... Discriminator Loss: 0.4235... Generator Loss: 3.2125
Epoch 10/10... Discriminator Loss: 0.4107... Generator Loss: 2.8436
Epoch 10/10... Discriminator Loss: 0.4008... Generator Loss: 4.0201
Epoch 10/10... Discriminator Loss: 0.4085... Generator Loss: 3.1248
Epoch 10/10... Discriminator Loss: 0.3934... Generator Loss: 4.1271
Epoch 10/10... Discriminator Loss: 0.4161... Generator Loss: 3.6876
Epoch 10/10... Discriminator Loss: 0.4380... Generator Loss: 2.9470
Epoch 10/10... Discriminator Loss: 0.3908... Generator Loss: 4.0015
Epoch 10/10... Discriminator Loss: 0.3970... Generator Loss: 4.1515
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.4240... Generator Loss: 4.6034
Epoch 10/10... Discriminator Loss: 0.6022... Generator Loss: 1.9414
Epoch 10/10... Discriminator Loss: 0.6033... Generator Loss: 3.0580
Epoch 10/10... Discriminator Loss: 0.4458... Generator Loss: 2.7627
Epoch 10/10... Discriminator Loss: 0.4576... Generator Loss: 2.6726
Epoch 10/10... Discriminator Loss: 0.4610... Generator Loss: 2.7493
Epoch 10/10... Discriminator Loss: 0.5831... Generator Loss: 2.5923
Epoch 10/10... Discriminator Loss: 0.4456... Generator Loss: 3.6815

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [79]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
100
(202599, 28, 28, 3)
TYPE input_z: <class 'tensorflow.python.framework.ops.Tensor'>
Epoch 1/10... Discriminator Loss: 0.6541... Generator Loss: 1.9731
Epoch 1/10... Discriminator Loss: 0.5541... Generator Loss: 2.6327
Epoch 1/10... Discriminator Loss: 0.4826... Generator Loss: 3.1824
Epoch 1/10... Discriminator Loss: 0.4684... Generator Loss: 3.6953
Epoch 1/10... Discriminator Loss: 0.5236... Generator Loss: 2.9292
Epoch 1/10... Discriminator Loss: 0.9928... Generator Loss: 1.2732
Epoch 1/10... Discriminator Loss: 0.5257... Generator Loss: 2.9233
Epoch 1/10... Discriminator Loss: 0.4398... Generator Loss: 4.1845
Epoch 1/10... Discriminator Loss: 0.4377... Generator Loss: 3.8443
Epoch 1/10... Discriminator Loss: 0.4213... Generator Loss: 4.0256
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.9513... Generator Loss: 1.1369
Epoch 1/10... Discriminator Loss: 0.5470... Generator Loss: 2.6531
Epoch 1/10... Discriminator Loss: 0.4255... Generator Loss: 3.8012
Epoch 1/10... Discriminator Loss: 0.3935... Generator Loss: 4.2323
Epoch 1/10... Discriminator Loss: 0.3948... Generator Loss: 4.2362
Epoch 1/10... Discriminator Loss: 0.4074... Generator Loss: 4.2604
Epoch 1/10... Discriminator Loss: 0.3958... Generator Loss: 4.0241
Epoch 1/10... Discriminator Loss: 0.3752... Generator Loss: 4.4039
Epoch 1/10... Discriminator Loss: 0.3756... Generator Loss: 4.2675
Epoch 1/10... Discriminator Loss: 0.3620... Generator Loss: 5.1063
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.3587... Generator Loss: 5.2418
Epoch 1/10... Discriminator Loss: 0.3770... Generator Loss: 4.4526
Epoch 1/10... Discriminator Loss: 0.4609... Generator Loss: 2.9416
Epoch 1/10... Discriminator Loss: 0.6854... Generator Loss: 9.2820
Epoch 1/10... Discriminator Loss: 0.4796... Generator Loss: 3.2665
Epoch 1/10... Discriminator Loss: 0.4091... Generator Loss: 3.8530
Epoch 1/10... Discriminator Loss: 0.4071... Generator Loss: 4.0135
Epoch 1/10... Discriminator Loss: 0.5777... Generator Loss: 2.0219
Epoch 1/10... Discriminator Loss: 0.4875... Generator Loss: 3.1435
Epoch 1/10... Discriminator Loss: 0.5089... Generator Loss: 3.3975
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.6607... Generator Loss: 1.7769
Epoch 1/10... Discriminator Loss: 0.4991... Generator Loss: 2.6650
Epoch 1/10... Discriminator Loss: 1.0040... Generator Loss: 3.6060
Epoch 1/10... Discriminator Loss: 0.9298... Generator Loss: 1.1826
Epoch 1/10... Discriminator Loss: 0.7106... Generator Loss: 1.6078
Epoch 1/10... Discriminator Loss: 0.7644... Generator Loss: 3.2786
Epoch 1/10... Discriminator Loss: 2.0388... Generator Loss: 0.3747
Epoch 1/10... Discriminator Loss: 0.7890... Generator Loss: 1.6816
Epoch 1/10... Discriminator Loss: 0.7391... Generator Loss: 1.7885
Epoch 1/10... Discriminator Loss: 1.0072... Generator Loss: 1.3732
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.6891... Generator Loss: 1.9162
Epoch 1/10... Discriminator Loss: 0.5622... Generator Loss: 3.5199
Epoch 1/10... Discriminator Loss: 0.5125... Generator Loss: 4.5742
Epoch 1/10... Discriminator Loss: 0.9070... Generator Loss: 3.4561
Epoch 1/10... Discriminator Loss: 0.8254... Generator Loss: 1.8699
Epoch 1/10... Discriminator Loss: 1.3568... Generator Loss: 0.6081
Epoch 1/10... Discriminator Loss: 0.7972... Generator Loss: 1.4095
Epoch 1/10... Discriminator Loss: 0.8526... Generator Loss: 1.4038
Epoch 1/10... Discriminator Loss: 0.6913... Generator Loss: 2.7616
Epoch 1/10... Discriminator Loss: 1.6235... Generator Loss: 0.4969
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.8593... Generator Loss: 1.6481
Epoch 1/10... Discriminator Loss: 1.2266... Generator Loss: 3.9231
Epoch 1/10... Discriminator Loss: 0.5803... Generator Loss: 2.3027
Epoch 1/10... Discriminator Loss: 0.6609... Generator Loss: 3.0402
Epoch 1/10... Discriminator Loss: 0.9099... Generator Loss: 1.5164
Epoch 1/10... Discriminator Loss: 0.6834... Generator Loss: 1.7469
Epoch 1/10... Discriminator Loss: 0.9373... Generator Loss: 1.4370
Epoch 1/10... Discriminator Loss: 0.8315... Generator Loss: 1.7257
Epoch 1/10... Discriminator Loss: 1.1186... Generator Loss: 2.7511
Epoch 1/10... Discriminator Loss: 1.3537... Generator Loss: 2.6091
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.7644... Generator Loss: 1.6161
Epoch 1/10... Discriminator Loss: 1.2585... Generator Loss: 0.7083
Epoch 1/10... Discriminator Loss: 1.2607... Generator Loss: 2.2846
Epoch 1/10... Discriminator Loss: 1.0924... Generator Loss: 1.1570
Epoch 1/10... Discriminator Loss: 0.8894... Generator Loss: 1.8171
Epoch 1/10... Discriminator Loss: 0.9290... Generator Loss: 1.5607
Epoch 1/10... Discriminator Loss: 1.0219... Generator Loss: 1.1659
Epoch 1/10... Discriminator Loss: 1.1209... Generator Loss: 1.2318
Epoch 1/10... Discriminator Loss: 1.0198... Generator Loss: 1.6015
Epoch 1/10... Discriminator Loss: 1.2301... Generator Loss: 0.9018
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.9293... Generator Loss: 2.2850
Epoch 1/10... Discriminator Loss: 0.8720... Generator Loss: 1.2644
Epoch 1/10... Discriminator Loss: 1.2773... Generator Loss: 1.1836
Epoch 1/10... Discriminator Loss: 0.9989... Generator Loss: 1.7263
Epoch 1/10... Discriminator Loss: 0.9964... Generator Loss: 1.7318
Epoch 1/10... Discriminator Loss: 0.9517... Generator Loss: 1.3127
Epoch 1/10... Discriminator Loss: 1.0936... Generator Loss: 1.2081
Epoch 1/10... Discriminator Loss: 1.0694... Generator Loss: 1.2398
Epoch 1/10... Discriminator Loss: 1.1063... Generator Loss: 3.1648
Epoch 1/10... Discriminator Loss: 0.9381... Generator Loss: 1.2961
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.0507... Generator Loss: 2.0108
Epoch 1/10... Discriminator Loss: 1.3523... Generator Loss: 0.8052
Epoch 1/10... Discriminator Loss: 1.1029... Generator Loss: 1.4272
Epoch 1/10... Discriminator Loss: 1.0319... Generator Loss: 1.4399
Epoch 1/10... Discriminator Loss: 1.0228... Generator Loss: 1.1389
Epoch 1/10... Discriminator Loss: 1.0240... Generator Loss: 1.3910
Epoch 1/10... Discriminator Loss: 1.0677... Generator Loss: 1.1337
Epoch 1/10... Discriminator Loss: 0.9756... Generator Loss: 1.2481
Epoch 1/10... Discriminator Loss: 1.0004... Generator Loss: 1.3075
Epoch 1/10... Discriminator Loss: 1.0780... Generator Loss: 1.2007
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.0692... Generator Loss: 1.5995
Epoch 1/10... Discriminator Loss: 1.1850... Generator Loss: 1.1640
Epoch 1/10... Discriminator Loss: 0.8911... Generator Loss: 1.4405
Epoch 1/10... Discriminator Loss: 1.0492... Generator Loss: 1.0895
Epoch 1/10... Discriminator Loss: 1.1461... Generator Loss: 1.2382
Epoch 1/10... Discriminator Loss: 1.3493... Generator Loss: 0.9064
Epoch 1/10... Discriminator Loss: 0.9162... Generator Loss: 1.5651
Epoch 1/10... Discriminator Loss: 1.4863... Generator Loss: 0.6469
Epoch 1/10... Discriminator Loss: 1.2115... Generator Loss: 0.9534
Epoch 1/10... Discriminator Loss: 1.2518... Generator Loss: 1.4347
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.0371... Generator Loss: 1.5298
Epoch 1/10... Discriminator Loss: 1.0820... Generator Loss: 1.6420
Epoch 1/10... Discriminator Loss: 0.8313... Generator Loss: 1.4434
Epoch 1/10... Discriminator Loss: 1.0270... Generator Loss: 1.6027
Epoch 1/10... Discriminator Loss: 1.1540... Generator Loss: 0.7997
Epoch 1/10... Discriminator Loss: 1.0629... Generator Loss: 1.2240
Epoch 1/10... Discriminator Loss: 1.1759... Generator Loss: 2.2474
Epoch 1/10... Discriminator Loss: 1.2262... Generator Loss: 1.0017
Epoch 1/10... Discriminator Loss: 1.0716... Generator Loss: 1.5184
Epoch 1/10... Discriminator Loss: 0.8083... Generator Loss: 1.7091
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.0378... Generator Loss: 1.0296
Epoch 1/10... Discriminator Loss: 1.1799... Generator Loss: 1.6493
Epoch 1/10... Discriminator Loss: 1.1431... Generator Loss: 1.7926
Epoch 1/10... Discriminator Loss: 1.0707... Generator Loss: 1.7111
Epoch 1/10... Discriminator Loss: 1.0337... Generator Loss: 1.2084
Epoch 1/10... Discriminator Loss: 1.3661... Generator Loss: 1.8275
Epoch 1/10... Discriminator Loss: 1.1143... Generator Loss: 1.4769
Epoch 1/10... Discriminator Loss: 1.1257... Generator Loss: 1.4933
Epoch 1/10... Discriminator Loss: 1.1213... Generator Loss: 0.8794
Epoch 1/10... Discriminator Loss: 1.0610... Generator Loss: 1.8259
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 0.9280... Generator Loss: 2.0994
Epoch 1/10... Discriminator Loss: 1.3201... Generator Loss: 0.9442
Epoch 1/10... Discriminator Loss: 1.1193... Generator Loss: 1.1076
Epoch 1/10... Discriminator Loss: 1.1859... Generator Loss: 1.0272
Epoch 1/10... Discriminator Loss: 0.9807... Generator Loss: 1.2779
Epoch 1/10... Discriminator Loss: 1.0937... Generator Loss: 0.9852
Epoch 1/10... Discriminator Loss: 1.5231... Generator Loss: 2.7415
Epoch 1/10... Discriminator Loss: 1.0398... Generator Loss: 1.6080
Epoch 1/10... Discriminator Loss: 0.9082... Generator Loss: 1.4671
Epoch 1/10... Discriminator Loss: 0.9431... Generator Loss: 1.1563
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.1125... Generator Loss: 1.4931
Epoch 1/10... Discriminator Loss: 1.0396... Generator Loss: 1.4679
Epoch 1/10... Discriminator Loss: 1.2139... Generator Loss: 0.9457
Epoch 1/10... Discriminator Loss: 0.9967... Generator Loss: 1.5561
Epoch 1/10... Discriminator Loss: 0.9742... Generator Loss: 1.2026
Epoch 1/10... Discriminator Loss: 1.0530... Generator Loss: 1.0550
Epoch 1/10... Discriminator Loss: 1.0012... Generator Loss: 1.8975
Epoch 1/10... Discriminator Loss: 1.1193... Generator Loss: 0.8938
Epoch 1/10... Discriminator Loss: 1.1327... Generator Loss: 1.3625
Epoch 1/10... Discriminator Loss: 1.1993... Generator Loss: 0.8254
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.2825... Generator Loss: 1.0527
Epoch 1/10... Discriminator Loss: 1.0993... Generator Loss: 1.2180
Epoch 1/10... Discriminator Loss: 1.1612... Generator Loss: 1.0081
Epoch 1/10... Discriminator Loss: 1.1639... Generator Loss: 1.3330
Epoch 1/10... Discriminator Loss: 1.0285... Generator Loss: 1.1190
Epoch 1/10... Discriminator Loss: 1.0548... Generator Loss: 1.3471
Epoch 1/10... Discriminator Loss: 1.1466... Generator Loss: 1.0167
Epoch 1/10... Discriminator Loss: 0.9716... Generator Loss: 1.5821
Epoch 1/10... Discriminator Loss: 1.2048... Generator Loss: 0.8829
Epoch 1/10... Discriminator Loss: 1.0408... Generator Loss: 1.3212
<class 'numpy.ndarray'>
Epoch 1/10... Discriminator Loss: 1.0108... Generator Loss: 1.1870
Epoch 1/10... Discriminator Loss: 1.1862... Generator Loss: 0.9729
Epoch 1/10... Discriminator Loss: 1.0130... Generator Loss: 1.6061
Epoch 1/10... Discriminator Loss: 1.0112... Generator Loss: 1.4255
Epoch 1/10... Discriminator Loss: 1.2709... Generator Loss: 0.9963
Epoch 1/10... Discriminator Loss: 1.0122... Generator Loss: 1.1551
Epoch 1/10... Discriminator Loss: 1.0618... Generator Loss: 1.2912
Epoch 1/10... Discriminator Loss: 1.0972... Generator Loss: 1.1822
Epoch 2/10... Discriminator Loss: 1.0723... Generator Loss: 1.1020
Epoch 2/10... Discriminator Loss: 1.1741... Generator Loss: 1.5726
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0182... Generator Loss: 1.6410
Epoch 2/10... Discriminator Loss: 1.2994... Generator Loss: 1.7413
Epoch 2/10... Discriminator Loss: 1.2049... Generator Loss: 0.9042
Epoch 2/10... Discriminator Loss: 1.3225... Generator Loss: 0.9003
Epoch 2/10... Discriminator Loss: 1.2509... Generator Loss: 0.8892
Epoch 2/10... Discriminator Loss: 1.0586... Generator Loss: 1.2146
Epoch 2/10... Discriminator Loss: 1.2365... Generator Loss: 0.7933
Epoch 2/10... Discriminator Loss: 1.0249... Generator Loss: 1.2943
Epoch 2/10... Discriminator Loss: 1.1454... Generator Loss: 0.8868
Epoch 2/10... Discriminator Loss: 1.1548... Generator Loss: 1.5861
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.1279... Generator Loss: 1.3413
Epoch 2/10... Discriminator Loss: 1.2487... Generator Loss: 0.8189
Epoch 2/10... Discriminator Loss: 1.4166... Generator Loss: 0.8530
Epoch 2/10... Discriminator Loss: 1.1722... Generator Loss: 1.0974
Epoch 2/10... Discriminator Loss: 1.1305... Generator Loss: 1.2988
Epoch 2/10... Discriminator Loss: 1.1530... Generator Loss: 1.2114
Epoch 2/10... Discriminator Loss: 1.2849... Generator Loss: 0.6432
Epoch 2/10... Discriminator Loss: 1.0501... Generator Loss: 0.9471
Epoch 2/10... Discriminator Loss: 1.1870... Generator Loss: 1.2160
Epoch 2/10... Discriminator Loss: 1.2020... Generator Loss: 0.9260
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0162... Generator Loss: 1.1886
Epoch 2/10... Discriminator Loss: 0.8492... Generator Loss: 1.4592
Epoch 2/10... Discriminator Loss: 1.5476... Generator Loss: 0.6223
Epoch 2/10... Discriminator Loss: 0.9611... Generator Loss: 1.3873
Epoch 2/10... Discriminator Loss: 0.9575... Generator Loss: 1.2461
Epoch 2/10... Discriminator Loss: 1.0104... Generator Loss: 1.3709
Epoch 2/10... Discriminator Loss: 1.0165... Generator Loss: 1.1212
Epoch 2/10... Discriminator Loss: 0.9411... Generator Loss: 1.5076
Epoch 2/10... Discriminator Loss: 1.4710... Generator Loss: 1.0807
Epoch 2/10... Discriminator Loss: 1.2798... Generator Loss: 1.1840
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 0.9694... Generator Loss: 1.5581
Epoch 2/10... Discriminator Loss: 1.1688... Generator Loss: 1.2445
Epoch 2/10... Discriminator Loss: 1.1265... Generator Loss: 1.3170
Epoch 2/10... Discriminator Loss: 1.3436... Generator Loss: 0.6851
Epoch 2/10... Discriminator Loss: 1.2978... Generator Loss: 1.0485
Epoch 2/10... Discriminator Loss: 0.8698... Generator Loss: 1.6519
Epoch 2/10... Discriminator Loss: 1.0208... Generator Loss: 1.2926
Epoch 2/10... Discriminator Loss: 1.1115... Generator Loss: 1.3982
Epoch 2/10... Discriminator Loss: 1.0480... Generator Loss: 1.1738
Epoch 2/10... Discriminator Loss: 0.9937... Generator Loss: 0.9694
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 0.8752... Generator Loss: 2.2767
Epoch 2/10... Discriminator Loss: 1.2639... Generator Loss: 0.6913
Epoch 2/10... Discriminator Loss: 1.0946... Generator Loss: 1.3167
Epoch 2/10... Discriminator Loss: 0.8642... Generator Loss: 1.6782
Epoch 2/10... Discriminator Loss: 1.1110... Generator Loss: 1.1095
Epoch 2/10... Discriminator Loss: 1.2217... Generator Loss: 1.8604
Epoch 2/10... Discriminator Loss: 1.1457... Generator Loss: 1.1878
Epoch 2/10... Discriminator Loss: 0.9599... Generator Loss: 1.3052
Epoch 2/10... Discriminator Loss: 1.4577... Generator Loss: 0.5522
Epoch 2/10... Discriminator Loss: 0.9064... Generator Loss: 1.1530
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0315... Generator Loss: 1.6223
Epoch 2/10... Discriminator Loss: 1.1686... Generator Loss: 1.0832
Epoch 2/10... Discriminator Loss: 1.2650... Generator Loss: 0.7236
Epoch 2/10... Discriminator Loss: 0.9090... Generator Loss: 1.6744
Epoch 2/10... Discriminator Loss: 0.9304... Generator Loss: 1.5640
Epoch 2/10... Discriminator Loss: 1.0782... Generator Loss: 1.1082
Epoch 2/10... Discriminator Loss: 1.5528... Generator Loss: 0.5838
Epoch 2/10... Discriminator Loss: 1.0092... Generator Loss: 1.7654
Epoch 2/10... Discriminator Loss: 1.1369... Generator Loss: 1.1873
Epoch 2/10... Discriminator Loss: 1.1054... Generator Loss: 0.9481
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.3179... Generator Loss: 0.8618
Epoch 2/10... Discriminator Loss: 1.1168... Generator Loss: 1.0420
Epoch 2/10... Discriminator Loss: 1.2332... Generator Loss: 1.5355
Epoch 2/10... Discriminator Loss: 1.0726... Generator Loss: 1.4763
Epoch 2/10... Discriminator Loss: 1.0756... Generator Loss: 1.0394
Epoch 2/10... Discriminator Loss: 0.9883... Generator Loss: 1.2436
Epoch 2/10... Discriminator Loss: 0.9155... Generator Loss: 1.3027
Epoch 2/10... Discriminator Loss: 1.1247... Generator Loss: 1.0653
Epoch 2/10... Discriminator Loss: 0.9378... Generator Loss: 1.4320
Epoch 2/10... Discriminator Loss: 1.0947... Generator Loss: 1.2894
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.4318... Generator Loss: 0.5391
Epoch 2/10... Discriminator Loss: 1.1482... Generator Loss: 0.9587
Epoch 2/10... Discriminator Loss: 1.1471... Generator Loss: 1.0338
Epoch 2/10... Discriminator Loss: 1.0600... Generator Loss: 1.5768
Epoch 2/10... Discriminator Loss: 0.8618... Generator Loss: 1.6231
Epoch 2/10... Discriminator Loss: 1.1829... Generator Loss: 1.0346
Epoch 2/10... Discriminator Loss: 1.1027... Generator Loss: 1.5130
Epoch 2/10... Discriminator Loss: 1.0907... Generator Loss: 1.3042
Epoch 2/10... Discriminator Loss: 1.1215... Generator Loss: 1.3549
Epoch 2/10... Discriminator Loss: 1.1895... Generator Loss: 1.0129
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.0215... Generator Loss: 1.1465
Epoch 2/10... Discriminator Loss: 1.2518... Generator Loss: 0.9916
Epoch 2/10... Discriminator Loss: 1.2586... Generator Loss: 0.7494
Epoch 2/10... Discriminator Loss: 1.0967... Generator Loss: 0.8424
Epoch 2/10... Discriminator Loss: 1.1324... Generator Loss: 1.6408
Epoch 2/10... Discriminator Loss: 1.0154... Generator Loss: 1.4614
Epoch 2/10... Discriminator Loss: 1.0697... Generator Loss: 1.1478
Epoch 2/10... Discriminator Loss: 1.0357... Generator Loss: 1.0593
Epoch 2/10... Discriminator Loss: 1.0340... Generator Loss: 1.7472
Epoch 2/10... Discriminator Loss: 1.1238... Generator Loss: 1.3494
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.1432... Generator Loss: 1.1062
Epoch 2/10... Discriminator Loss: 1.0793... Generator Loss: 1.0102
Epoch 2/10... Discriminator Loss: 1.0476... Generator Loss: 0.9930
Epoch 2/10... Discriminator Loss: 1.2586... Generator Loss: 0.8879
Epoch 2/10... Discriminator Loss: 1.6000... Generator Loss: 0.7470
Epoch 2/10... Discriminator Loss: 1.2305... Generator Loss: 1.3938
Epoch 2/10... Discriminator Loss: 1.0441... Generator Loss: 1.3591
Epoch 2/10... Discriminator Loss: 1.1551... Generator Loss: 1.1366
Epoch 2/10... Discriminator Loss: 0.9836... Generator Loss: 1.3793
Epoch 2/10... Discriminator Loss: 1.3164... Generator Loss: 0.7868
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.2314... Generator Loss: 1.5047
Epoch 2/10... Discriminator Loss: 1.0111... Generator Loss: 1.2841
Epoch 2/10... Discriminator Loss: 1.0699... Generator Loss: 1.3720
Epoch 2/10... Discriminator Loss: 1.1224... Generator Loss: 0.9062
Epoch 2/10... Discriminator Loss: 1.0446... Generator Loss: 1.5271
Epoch 2/10... Discriminator Loss: 1.3330... Generator Loss: 0.7403
Epoch 2/10... Discriminator Loss: 1.1402... Generator Loss: 0.9580
Epoch 2/10... Discriminator Loss: 0.9841... Generator Loss: 1.3521
Epoch 2/10... Discriminator Loss: 1.0390... Generator Loss: 1.2153
Epoch 2/10... Discriminator Loss: 1.3997... Generator Loss: 0.9790
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.3011... Generator Loss: 0.8711
Epoch 2/10... Discriminator Loss: 0.9785... Generator Loss: 1.4147
Epoch 2/10... Discriminator Loss: 1.0555... Generator Loss: 1.0852
Epoch 2/10... Discriminator Loss: 1.3205... Generator Loss: 1.2596
Epoch 2/10... Discriminator Loss: 1.2229... Generator Loss: 0.7472
Epoch 2/10... Discriminator Loss: 0.9261... Generator Loss: 1.3188
Epoch 2/10... Discriminator Loss: 1.1996... Generator Loss: 0.8735
Epoch 2/10... Discriminator Loss: 1.0289... Generator Loss: 1.3499
Epoch 2/10... Discriminator Loss: 1.1158... Generator Loss: 1.1475
Epoch 2/10... Discriminator Loss: 1.0059... Generator Loss: 1.0696
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 0.9345... Generator Loss: 1.1598
Epoch 2/10... Discriminator Loss: 1.1446... Generator Loss: 1.1529
Epoch 2/10... Discriminator Loss: 1.1485... Generator Loss: 1.4622
Epoch 2/10... Discriminator Loss: 1.1326... Generator Loss: 1.3514
Epoch 2/10... Discriminator Loss: 1.1192... Generator Loss: 1.0220
Epoch 2/10... Discriminator Loss: 0.9917... Generator Loss: 1.2290
Epoch 2/10... Discriminator Loss: 1.3748... Generator Loss: 1.7615
Epoch 2/10... Discriminator Loss: 1.6491... Generator Loss: 0.4345
Epoch 2/10... Discriminator Loss: 1.1228... Generator Loss: 1.2557
Epoch 2/10... Discriminator Loss: 1.2069... Generator Loss: 0.8907
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.2726... Generator Loss: 1.3284
Epoch 2/10... Discriminator Loss: 1.0557... Generator Loss: 0.8933
Epoch 2/10... Discriminator Loss: 1.7395... Generator Loss: 0.4702
Epoch 2/10... Discriminator Loss: 1.1166... Generator Loss: 1.0945
Epoch 2/10... Discriminator Loss: 0.8588... Generator Loss: 1.8316
Epoch 2/10... Discriminator Loss: 1.0072... Generator Loss: 1.5307
Epoch 2/10... Discriminator Loss: 1.2276... Generator Loss: 1.9229
Epoch 2/10... Discriminator Loss: 1.1319... Generator Loss: 1.4103
Epoch 2/10... Discriminator Loss: 0.9997... Generator Loss: 1.0725
Epoch 2/10... Discriminator Loss: 1.1163... Generator Loss: 1.4522
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.2523... Generator Loss: 0.9280
Epoch 2/10... Discriminator Loss: 0.9666... Generator Loss: 1.3021
Epoch 2/10... Discriminator Loss: 0.8230... Generator Loss: 1.5474
Epoch 2/10... Discriminator Loss: 1.0080... Generator Loss: 1.3317
Epoch 2/10... Discriminator Loss: 1.0798... Generator Loss: 1.1007
Epoch 2/10... Discriminator Loss: 0.7778... Generator Loss: 1.9582
Epoch 2/10... Discriminator Loss: 1.2222... Generator Loss: 1.5697
Epoch 2/10... Discriminator Loss: 0.9598... Generator Loss: 1.3429
Epoch 2/10... Discriminator Loss: 0.8404... Generator Loss: 1.2843
Epoch 2/10... Discriminator Loss: 0.9035... Generator Loss: 1.3850
<class 'numpy.ndarray'>
Epoch 2/10... Discriminator Loss: 1.1937... Generator Loss: 1.0296
Epoch 2/10... Discriminator Loss: 1.1509... Generator Loss: 2.1262
Epoch 2/10... Discriminator Loss: 1.1365... Generator Loss: 1.0819
Epoch 2/10... Discriminator Loss: 1.3127... Generator Loss: 0.9020
Epoch 2/10... Discriminator Loss: 0.9644... Generator Loss: 1.2350
Epoch 2/10... Discriminator Loss: 1.2277... Generator Loss: 1.6324
Epoch 3/10... Discriminator Loss: 1.1153... Generator Loss: 1.1361
Epoch 3/10... Discriminator Loss: 0.8548... Generator Loss: 1.1779
Epoch 3/10... Discriminator Loss: 1.0809... Generator Loss: 1.3693
Epoch 3/10... Discriminator Loss: 0.8377... Generator Loss: 1.6115
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.9869... Generator Loss: 1.5098
Epoch 3/10... Discriminator Loss: 1.2629... Generator Loss: 0.7686
Epoch 3/10... Discriminator Loss: 1.0021... Generator Loss: 1.4370
Epoch 3/10... Discriminator Loss: 1.2631... Generator Loss: 0.9731
Epoch 3/10... Discriminator Loss: 1.0054... Generator Loss: 1.1550
Epoch 3/10... Discriminator Loss: 1.1633... Generator Loss: 1.5183
Epoch 3/10... Discriminator Loss: 1.1776... Generator Loss: 1.1929
Epoch 3/10... Discriminator Loss: 1.0366... Generator Loss: 1.1795
Epoch 3/10... Discriminator Loss: 1.0407... Generator Loss: 1.2897
Epoch 3/10... Discriminator Loss: 0.9730... Generator Loss: 1.1098
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.1007... Generator Loss: 0.9325
Epoch 3/10... Discriminator Loss: 1.0367... Generator Loss: 0.9771
Epoch 3/10... Discriminator Loss: 1.0139... Generator Loss: 1.0317
Epoch 3/10... Discriminator Loss: 1.0946... Generator Loss: 1.2331
Epoch 3/10... Discriminator Loss: 1.1532... Generator Loss: 1.1336
Epoch 3/10... Discriminator Loss: 0.9971... Generator Loss: 1.0554
Epoch 3/10... Discriminator Loss: 1.1650... Generator Loss: 1.6412
Epoch 3/10... Discriminator Loss: 1.2202... Generator Loss: 2.0022
Epoch 3/10... Discriminator Loss: 1.2081... Generator Loss: 0.8245
Epoch 3/10... Discriminator Loss: 0.8715... Generator Loss: 1.4326
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.1393... Generator Loss: 1.3563
Epoch 3/10... Discriminator Loss: 0.8865... Generator Loss: 1.7854
Epoch 3/10... Discriminator Loss: 1.7155... Generator Loss: 0.3490
Epoch 3/10... Discriminator Loss: 1.3608... Generator Loss: 0.9016
Epoch 3/10... Discriminator Loss: 0.9990... Generator Loss: 1.3519
Epoch 3/10... Discriminator Loss: 1.1911... Generator Loss: 0.8588
Epoch 3/10... Discriminator Loss: 1.0478... Generator Loss: 1.0182
Epoch 3/10... Discriminator Loss: 0.9434... Generator Loss: 1.9868
Epoch 3/10... Discriminator Loss: 0.8215... Generator Loss: 1.4234
Epoch 3/10... Discriminator Loss: 1.0743... Generator Loss: 1.2750
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.0793... Generator Loss: 1.2917
Epoch 3/10... Discriminator Loss: 1.1855... Generator Loss: 0.9960
Epoch 3/10... Discriminator Loss: 0.9651... Generator Loss: 1.2532
Epoch 3/10... Discriminator Loss: 0.9691... Generator Loss: 1.6920
Epoch 3/10... Discriminator Loss: 1.4185... Generator Loss: 1.3558
Epoch 3/10... Discriminator Loss: 1.1070... Generator Loss: 1.3426
Epoch 3/10... Discriminator Loss: 0.9324... Generator Loss: 1.3457
Epoch 3/10... Discriminator Loss: 0.9985... Generator Loss: 1.6440
Epoch 3/10... Discriminator Loss: 0.9774... Generator Loss: 1.5176
Epoch 3/10... Discriminator Loss: 0.9638... Generator Loss: 1.4250
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.0699... Generator Loss: 1.0159
Epoch 3/10... Discriminator Loss: 1.3449... Generator Loss: 0.8618
Epoch 3/10... Discriminator Loss: 1.1081... Generator Loss: 1.4271
Epoch 3/10... Discriminator Loss: 0.9612... Generator Loss: 1.1939
Epoch 3/10... Discriminator Loss: 1.1825... Generator Loss: 1.8889
Epoch 3/10... Discriminator Loss: 1.1197... Generator Loss: 0.9850
Epoch 3/10... Discriminator Loss: 0.9662... Generator Loss: 1.4743
Epoch 3/10... Discriminator Loss: 1.3336... Generator Loss: 0.6357
Epoch 3/10... Discriminator Loss: 1.6316... Generator Loss: 0.4987
Epoch 3/10... Discriminator Loss: 1.2795... Generator Loss: 0.8702
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8808... Generator Loss: 1.2917
Epoch 3/10... Discriminator Loss: 1.0696... Generator Loss: 0.8065
Epoch 3/10... Discriminator Loss: 0.7511... Generator Loss: 1.4026
Epoch 3/10... Discriminator Loss: 1.1732... Generator Loss: 0.9119
Epoch 3/10... Discriminator Loss: 1.1677... Generator Loss: 1.9461
Epoch 3/10... Discriminator Loss: 1.3140... Generator Loss: 0.8647
Epoch 3/10... Discriminator Loss: 1.0210... Generator Loss: 1.1656
Epoch 3/10... Discriminator Loss: 1.2662... Generator Loss: 0.8674
Epoch 3/10... Discriminator Loss: 1.1622... Generator Loss: 0.9083
Epoch 3/10... Discriminator Loss: 0.9330... Generator Loss: 1.2566
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8910... Generator Loss: 1.6684
Epoch 3/10... Discriminator Loss: 1.0601... Generator Loss: 1.5515
Epoch 3/10... Discriminator Loss: 1.2947... Generator Loss: 0.9667
Epoch 3/10... Discriminator Loss: 1.3255... Generator Loss: 1.6542
Epoch 3/10... Discriminator Loss: 1.1836... Generator Loss: 1.0842
Epoch 3/10... Discriminator Loss: 0.8293... Generator Loss: 1.7755
Epoch 3/10... Discriminator Loss: 1.0472... Generator Loss: 1.5164
Epoch 3/10... Discriminator Loss: 1.3209... Generator Loss: 1.0636
Epoch 3/10... Discriminator Loss: 1.1609... Generator Loss: 1.0502
Epoch 3/10... Discriminator Loss: 1.0696... Generator Loss: 1.6311
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8589... Generator Loss: 1.3618
Epoch 3/10... Discriminator Loss: 0.9443... Generator Loss: 1.3858
Epoch 3/10... Discriminator Loss: 1.0458... Generator Loss: 1.4840
Epoch 3/10... Discriminator Loss: 0.9146... Generator Loss: 2.0246
Epoch 3/10... Discriminator Loss: 1.0232... Generator Loss: 1.2487
Epoch 3/10... Discriminator Loss: 1.4434... Generator Loss: 1.3944
Epoch 3/10... Discriminator Loss: 1.0463... Generator Loss: 1.1500
Epoch 3/10... Discriminator Loss: 0.9889... Generator Loss: 0.9562
Epoch 3/10... Discriminator Loss: 1.4984... Generator Loss: 0.6759
Epoch 3/10... Discriminator Loss: 0.9154... Generator Loss: 2.1761
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.9979... Generator Loss: 1.3571
Epoch 3/10... Discriminator Loss: 1.0678... Generator Loss: 1.3127
Epoch 3/10... Discriminator Loss: 1.1727... Generator Loss: 0.7406
Epoch 3/10... Discriminator Loss: 1.2105... Generator Loss: 0.9219
Epoch 3/10... Discriminator Loss: 1.2947... Generator Loss: 0.7178
Epoch 3/10... Discriminator Loss: 1.3033... Generator Loss: 0.9217
Epoch 3/10... Discriminator Loss: 1.0814... Generator Loss: 1.3213
Epoch 3/10... Discriminator Loss: 1.1057... Generator Loss: 1.4056
Epoch 3/10... Discriminator Loss: 0.9896... Generator Loss: 1.1634
Epoch 3/10... Discriminator Loss: 1.1976... Generator Loss: 0.8596
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.3031... Generator Loss: 0.8520
Epoch 3/10... Discriminator Loss: 1.0735... Generator Loss: 0.9134
Epoch 3/10... Discriminator Loss: 0.7582... Generator Loss: 1.5949
Epoch 3/10... Discriminator Loss: 1.4213... Generator Loss: 0.6422
Epoch 3/10... Discriminator Loss: 1.1683... Generator Loss: 0.8792
Epoch 3/10... Discriminator Loss: 1.1481... Generator Loss: 1.6133
Epoch 3/10... Discriminator Loss: 1.0488... Generator Loss: 0.9745
Epoch 3/10... Discriminator Loss: 1.3714... Generator Loss: 0.5523
Epoch 3/10... Discriminator Loss: 1.1940... Generator Loss: 1.8395
Epoch 3/10... Discriminator Loss: 1.1711... Generator Loss: 1.1233
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.1886... Generator Loss: 0.7898
Epoch 3/10... Discriminator Loss: 1.4202... Generator Loss: 1.0345
Epoch 3/10... Discriminator Loss: 0.9506... Generator Loss: 1.0726
Epoch 3/10... Discriminator Loss: 1.0057... Generator Loss: 2.1328
Epoch 3/10... Discriminator Loss: 1.1069... Generator Loss: 1.1615
Epoch 3/10... Discriminator Loss: 1.8008... Generator Loss: 2.8368
Epoch 3/10... Discriminator Loss: 0.9323... Generator Loss: 1.5357
Epoch 3/10... Discriminator Loss: 1.5461... Generator Loss: 0.8404
Epoch 3/10... Discriminator Loss: 0.8948... Generator Loss: 1.9234
Epoch 3/10... Discriminator Loss: 0.9453... Generator Loss: 1.3392
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.0526... Generator Loss: 0.9953
Epoch 3/10... Discriminator Loss: 0.9663... Generator Loss: 1.2855
Epoch 3/10... Discriminator Loss: 1.1566... Generator Loss: 0.7693
Epoch 3/10... Discriminator Loss: 0.8336... Generator Loss: 1.6527
Epoch 3/10... Discriminator Loss: 0.8165... Generator Loss: 1.4744
Epoch 3/10... Discriminator Loss: 1.6798... Generator Loss: 0.6038
Epoch 3/10... Discriminator Loss: 1.2923... Generator Loss: 0.7769
Epoch 3/10... Discriminator Loss: 1.0709... Generator Loss: 1.1729
Epoch 3/10... Discriminator Loss: 1.3125... Generator Loss: 0.9104
Epoch 3/10... Discriminator Loss: 1.1007... Generator Loss: 1.3969
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.0234... Generator Loss: 1.1379
Epoch 3/10... Discriminator Loss: 1.1243... Generator Loss: 1.5346
Epoch 3/10... Discriminator Loss: 0.9314... Generator Loss: 1.5741
Epoch 3/10... Discriminator Loss: 0.8683... Generator Loss: 1.3508
Epoch 3/10... Discriminator Loss: 1.1714... Generator Loss: 0.9516
Epoch 3/10... Discriminator Loss: 1.5475... Generator Loss: 0.5225
Epoch 3/10... Discriminator Loss: 1.1865... Generator Loss: 1.0558
Epoch 3/10... Discriminator Loss: 0.8996... Generator Loss: 1.2502
Epoch 3/10... Discriminator Loss: 0.9416... Generator Loss: 1.3109
Epoch 3/10... Discriminator Loss: 0.8856... Generator Loss: 1.3361
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.0535... Generator Loss: 1.0533
Epoch 3/10... Discriminator Loss: 1.0168... Generator Loss: 1.1600
Epoch 3/10... Discriminator Loss: 1.4012... Generator Loss: 0.7652
Epoch 3/10... Discriminator Loss: 1.0604... Generator Loss: 0.8922
Epoch 3/10... Discriminator Loss: 0.6336... Generator Loss: 2.1568
Epoch 3/10... Discriminator Loss: 0.7836... Generator Loss: 1.4774
Epoch 3/10... Discriminator Loss: 1.0028... Generator Loss: 1.2077
Epoch 3/10... Discriminator Loss: 0.8919... Generator Loss: 1.8286
Epoch 3/10... Discriminator Loss: 1.1885... Generator Loss: 1.4021
Epoch 3/10... Discriminator Loss: 0.9061... Generator Loss: 1.2908
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 0.8204... Generator Loss: 1.5846
Epoch 3/10... Discriminator Loss: 1.0287... Generator Loss: 1.3657
Epoch 3/10... Discriminator Loss: 1.0547... Generator Loss: 1.2659
Epoch 3/10... Discriminator Loss: 1.2771... Generator Loss: 0.6556
Epoch 3/10... Discriminator Loss: 0.8552... Generator Loss: 1.2350
Epoch 3/10... Discriminator Loss: 0.5974... Generator Loss: 2.7257
Epoch 3/10... Discriminator Loss: 1.1429... Generator Loss: 1.1020
Epoch 3/10... Discriminator Loss: 0.7555... Generator Loss: 1.5540
Epoch 3/10... Discriminator Loss: 0.9547... Generator Loss: 1.1544
Epoch 3/10... Discriminator Loss: 1.2557... Generator Loss: 1.5295
<class 'numpy.ndarray'>
Epoch 3/10... Discriminator Loss: 1.3908... Generator Loss: 0.6560
Epoch 3/10... Discriminator Loss: 1.0170... Generator Loss: 1.1874
Epoch 3/10... Discriminator Loss: 0.7544... Generator Loss: 1.6615
Epoch 3/10... Discriminator Loss: 1.2867... Generator Loss: 0.7947
Epoch 4/10... Discriminator Loss: 0.7628... Generator Loss: 1.6708
Epoch 4/10... Discriminator Loss: 1.1891... Generator Loss: 2.2296
Epoch 4/10... Discriminator Loss: 0.9454... Generator Loss: 1.4459
Epoch 4/10... Discriminator Loss: 1.5717... Generator Loss: 0.7885
Epoch 4/10... Discriminator Loss: 1.2755... Generator Loss: 2.0743
Epoch 4/10... Discriminator Loss: 1.0112... Generator Loss: 1.3691
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.0153... Generator Loss: 1.2264
Epoch 4/10... Discriminator Loss: 1.0610... Generator Loss: 1.3459
Epoch 4/10... Discriminator Loss: 0.7074... Generator Loss: 2.2845
Epoch 4/10... Discriminator Loss: 0.9184... Generator Loss: 1.3809
Epoch 4/10... Discriminator Loss: 0.9190... Generator Loss: 1.6773
Epoch 4/10... Discriminator Loss: 0.9738... Generator Loss: 1.5897
Epoch 4/10... Discriminator Loss: 0.9420... Generator Loss: 1.3300
Epoch 4/10... Discriminator Loss: 1.1473... Generator Loss: 0.7898
Epoch 4/10... Discriminator Loss: 0.8857... Generator Loss: 1.1329
Epoch 4/10... Discriminator Loss: 1.0038... Generator Loss: 1.2217
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.0704... Generator Loss: 1.4385
Epoch 4/10... Discriminator Loss: 1.0363... Generator Loss: 1.3881
Epoch 4/10... Discriminator Loss: 0.9019... Generator Loss: 1.7547
Epoch 4/10... Discriminator Loss: 1.1304... Generator Loss: 0.9838
Epoch 4/10... Discriminator Loss: 1.0795... Generator Loss: 1.1565
Epoch 4/10... Discriminator Loss: 1.2707... Generator Loss: 0.9817
Epoch 4/10... Discriminator Loss: 1.0793... Generator Loss: 1.0055
Epoch 4/10... Discriminator Loss: 0.9465... Generator Loss: 1.6118
Epoch 4/10... Discriminator Loss: 0.8459... Generator Loss: 1.3609
Epoch 4/10... Discriminator Loss: 1.2094... Generator Loss: 1.0178
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.8377... Generator Loss: 1.6381
Epoch 4/10... Discriminator Loss: 1.0236... Generator Loss: 1.2148
Epoch 4/10... Discriminator Loss: 0.9343... Generator Loss: 1.4253
Epoch 4/10... Discriminator Loss: 1.1249... Generator Loss: 1.0372
Epoch 4/10... Discriminator Loss: 1.2870... Generator Loss: 0.7927
Epoch 4/10... Discriminator Loss: 1.2178... Generator Loss: 0.9895
Epoch 4/10... Discriminator Loss: 1.0788... Generator Loss: 1.7136
Epoch 4/10... Discriminator Loss: 0.7505... Generator Loss: 1.4060
Epoch 4/10... Discriminator Loss: 0.9749... Generator Loss: 1.7269
Epoch 4/10... Discriminator Loss: 1.2153... Generator Loss: 0.8229
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.9022... Generator Loss: 1.7533
Epoch 4/10... Discriminator Loss: 0.8323... Generator Loss: 1.4701
Epoch 4/10... Discriminator Loss: 1.1387... Generator Loss: 1.1975
Epoch 4/10... Discriminator Loss: 0.8698... Generator Loss: 1.2870
Epoch 4/10... Discriminator Loss: 0.9001... Generator Loss: 2.0062
Epoch 4/10... Discriminator Loss: 1.0003... Generator Loss: 1.1183
Epoch 4/10... Discriminator Loss: 1.0320... Generator Loss: 1.1648
Epoch 4/10... Discriminator Loss: 0.8840... Generator Loss: 1.4699
Epoch 4/10... Discriminator Loss: 0.9871... Generator Loss: 1.1784
Epoch 4/10... Discriminator Loss: 1.0327... Generator Loss: 1.3380
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.8431... Generator Loss: 2.2897
Epoch 4/10... Discriminator Loss: 0.9471... Generator Loss: 1.0945
Epoch 4/10... Discriminator Loss: 1.1200... Generator Loss: 2.1727
Epoch 4/10... Discriminator Loss: 1.2051... Generator Loss: 0.8786
Epoch 4/10... Discriminator Loss: 0.9595... Generator Loss: 1.7105
Epoch 4/10... Discriminator Loss: 1.1917... Generator Loss: 0.8399
Epoch 4/10... Discriminator Loss: 0.7329... Generator Loss: 1.7222
Epoch 4/10... Discriminator Loss: 0.8919... Generator Loss: 1.3936
Epoch 4/10... Discriminator Loss: 0.7747... Generator Loss: 1.4873
Epoch 4/10... Discriminator Loss: 1.1369... Generator Loss: 0.7688
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.9312... Generator Loss: 1.7780
Epoch 4/10... Discriminator Loss: 0.9684... Generator Loss: 1.2474
Epoch 4/10... Discriminator Loss: 0.7931... Generator Loss: 1.3275
Epoch 4/10... Discriminator Loss: 0.9970... Generator Loss: 1.2580
Epoch 4/10... Discriminator Loss: 0.7978... Generator Loss: 1.9476
Epoch 4/10... Discriminator Loss: 0.8461... Generator Loss: 1.2802
Epoch 4/10... Discriminator Loss: 0.8306... Generator Loss: 1.0724
Epoch 4/10... Discriminator Loss: 0.8856... Generator Loss: 1.2333
Epoch 4/10... Discriminator Loss: 0.8929... Generator Loss: 1.1211
Epoch 4/10... Discriminator Loss: 0.8569... Generator Loss: 1.6847
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.0275... Generator Loss: 1.1463
Epoch 4/10... Discriminator Loss: 0.8007... Generator Loss: 1.6056
Epoch 4/10... Discriminator Loss: 0.9396... Generator Loss: 1.2954
Epoch 4/10... Discriminator Loss: 1.1790... Generator Loss: 1.4638
Epoch 4/10... Discriminator Loss: 1.1967... Generator Loss: 1.5595
Epoch 4/10... Discriminator Loss: 1.1744... Generator Loss: 0.9163
Epoch 4/10... Discriminator Loss: 1.0984... Generator Loss: 0.9864
Epoch 4/10... Discriminator Loss: 1.3729... Generator Loss: 0.8250
Epoch 4/10... Discriminator Loss: 0.7174... Generator Loss: 1.7264
Epoch 4/10... Discriminator Loss: 0.9858... Generator Loss: 0.9688
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.0379... Generator Loss: 0.6874
Epoch 4/10... Discriminator Loss: 1.0116... Generator Loss: 1.2407
Epoch 4/10... Discriminator Loss: 1.0380... Generator Loss: 1.3773
Epoch 4/10... Discriminator Loss: 0.9382... Generator Loss: 1.0661
Epoch 4/10... Discriminator Loss: 0.7975... Generator Loss: 2.0791
Epoch 4/10... Discriminator Loss: 1.0968... Generator Loss: 1.0589
Epoch 4/10... Discriminator Loss: 0.8971... Generator Loss: 2.2232
Epoch 4/10... Discriminator Loss: 0.8697... Generator Loss: 1.5629
Epoch 4/10... Discriminator Loss: 1.3049... Generator Loss: 1.8285
Epoch 4/10... Discriminator Loss: 0.9758... Generator Loss: 1.1063
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.9230... Generator Loss: 1.2993
Epoch 4/10... Discriminator Loss: 0.9671... Generator Loss: 1.3716
Epoch 4/10... Discriminator Loss: 0.8137... Generator Loss: 1.4493
Epoch 4/10... Discriminator Loss: 1.0767... Generator Loss: 1.1428
Epoch 4/10... Discriminator Loss: 1.2953... Generator Loss: 1.1992
Epoch 4/10... Discriminator Loss: 1.1979... Generator Loss: 0.7000
Epoch 4/10... Discriminator Loss: 0.8957... Generator Loss: 1.3363
Epoch 4/10... Discriminator Loss: 0.9979... Generator Loss: 1.0021
Epoch 4/10... Discriminator Loss: 0.8465... Generator Loss: 1.0507
Epoch 4/10... Discriminator Loss: 0.9436... Generator Loss: 2.3723
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.1163... Generator Loss: 0.6604
Epoch 4/10... Discriminator Loss: 1.1115... Generator Loss: 0.9862
Epoch 4/10... Discriminator Loss: 0.8366... Generator Loss: 1.1252
Epoch 4/10... Discriminator Loss: 1.1911... Generator Loss: 1.3550
Epoch 4/10... Discriminator Loss: 0.8318... Generator Loss: 1.6784
Epoch 4/10... Discriminator Loss: 0.8701... Generator Loss: 1.7893
Epoch 4/10... Discriminator Loss: 0.8530... Generator Loss: 1.8452
Epoch 4/10... Discriminator Loss: 1.0132... Generator Loss: 1.0747
Epoch 4/10... Discriminator Loss: 1.0835... Generator Loss: 0.9072
Epoch 4/10... Discriminator Loss: 1.2298... Generator Loss: 1.1564
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.8547... Generator Loss: 1.4850
Epoch 4/10... Discriminator Loss: 1.0683... Generator Loss: 1.0927
Epoch 4/10... Discriminator Loss: 0.9558... Generator Loss: 1.3625
Epoch 4/10... Discriminator Loss: 1.2012... Generator Loss: 0.7439
Epoch 4/10... Discriminator Loss: 0.9699... Generator Loss: 1.3762
Epoch 4/10... Discriminator Loss: 1.1879... Generator Loss: 1.0618
Epoch 4/10... Discriminator Loss: 1.2438... Generator Loss: 0.8458
Epoch 4/10... Discriminator Loss: 0.9650... Generator Loss: 2.1324
Epoch 4/10... Discriminator Loss: 1.1328... Generator Loss: 0.8093
Epoch 4/10... Discriminator Loss: 0.8788... Generator Loss: 1.3451
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.5937... Generator Loss: 1.9149
Epoch 4/10... Discriminator Loss: 1.0760... Generator Loss: 0.8719
Epoch 4/10... Discriminator Loss: 1.0041... Generator Loss: 0.8745
Epoch 4/10... Discriminator Loss: 0.8768... Generator Loss: 1.2667
Epoch 4/10... Discriminator Loss: 0.9422... Generator Loss: 1.8874
Epoch 4/10... Discriminator Loss: 0.7595... Generator Loss: 1.6436
Epoch 4/10... Discriminator Loss: 1.2172... Generator Loss: 0.7601
Epoch 4/10... Discriminator Loss: 1.2429... Generator Loss: 0.6048
Epoch 4/10... Discriminator Loss: 1.1575... Generator Loss: 1.1880
Epoch 4/10... Discriminator Loss: 0.7713... Generator Loss: 1.8792
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.8423... Generator Loss: 1.1372
Epoch 4/10... Discriminator Loss: 1.1210... Generator Loss: 1.0985
Epoch 4/10... Discriminator Loss: 1.2558... Generator Loss: 1.1686
Epoch 4/10... Discriminator Loss: 0.9148... Generator Loss: 1.1583
Epoch 4/10... Discriminator Loss: 0.8261... Generator Loss: 1.4397
Epoch 4/10... Discriminator Loss: 0.9704... Generator Loss: 1.2213
Epoch 4/10... Discriminator Loss: 1.1076... Generator Loss: 1.9794
Epoch 4/10... Discriminator Loss: 0.7227... Generator Loss: 1.4565
Epoch 4/10... Discriminator Loss: 0.7944... Generator Loss: 1.4411
Epoch 4/10... Discriminator Loss: 1.3452... Generator Loss: 1.2022
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.4051... Generator Loss: 0.8559
Epoch 4/10... Discriminator Loss: 0.8476... Generator Loss: 1.3219
Epoch 4/10... Discriminator Loss: 0.8383... Generator Loss: 1.8585
Epoch 4/10... Discriminator Loss: 1.5411... Generator Loss: 2.9489
Epoch 4/10... Discriminator Loss: 1.0146... Generator Loss: 1.9233
Epoch 4/10... Discriminator Loss: 0.8817... Generator Loss: 1.0767
Epoch 4/10... Discriminator Loss: 0.7029... Generator Loss: 1.6175
Epoch 4/10... Discriminator Loss: 1.1024... Generator Loss: 0.9043
Epoch 4/10... Discriminator Loss: 1.1315... Generator Loss: 1.4384
Epoch 4/10... Discriminator Loss: 0.9263... Generator Loss: 1.0130
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 0.6111... Generator Loss: 1.9619
Epoch 4/10... Discriminator Loss: 0.7479... Generator Loss: 1.7089
Epoch 4/10... Discriminator Loss: 1.0819... Generator Loss: 0.9945
Epoch 4/10... Discriminator Loss: 0.8664... Generator Loss: 1.1618
Epoch 4/10... Discriminator Loss: 0.8679... Generator Loss: 1.5317
Epoch 4/10... Discriminator Loss: 0.6969... Generator Loss: 2.2157
Epoch 4/10... Discriminator Loss: 1.0934... Generator Loss: 0.9311
Epoch 4/10... Discriminator Loss: 0.8671... Generator Loss: 1.4229
Epoch 4/10... Discriminator Loss: 0.8401... Generator Loss: 1.9384
Epoch 4/10... Discriminator Loss: 0.8924... Generator Loss: 1.4491
<class 'numpy.ndarray'>
Epoch 4/10... Discriminator Loss: 1.0308... Generator Loss: 1.1124
Epoch 4/10... Discriminator Loss: 0.8194... Generator Loss: 1.6227
Epoch 5/10... Discriminator Loss: 1.3020... Generator Loss: 0.8325
Epoch 5/10... Discriminator Loss: 0.8918... Generator Loss: 1.1597
Epoch 5/10... Discriminator Loss: 0.9688... Generator Loss: 1.1461
Epoch 5/10... Discriminator Loss: 0.8482... Generator Loss: 1.3012
Epoch 5/10... Discriminator Loss: 0.7860... Generator Loss: 1.2657
Epoch 5/10... Discriminator Loss: 1.4246... Generator Loss: 0.7196
Epoch 5/10... Discriminator Loss: 0.6796... Generator Loss: 1.4084
Epoch 5/10... Discriminator Loss: 0.9587... Generator Loss: 1.1946
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.7771... Generator Loss: 1.3766
Epoch 5/10... Discriminator Loss: 0.8614... Generator Loss: 2.0811
Epoch 5/10... Discriminator Loss: 1.1008... Generator Loss: 1.4569
Epoch 5/10... Discriminator Loss: 0.9699... Generator Loss: 0.9630
Epoch 5/10... Discriminator Loss: 0.9775... Generator Loss: 1.1834
Epoch 5/10... Discriminator Loss: 1.1413... Generator Loss: 1.1736
Epoch 5/10... Discriminator Loss: 1.1319... Generator Loss: 1.1219
Epoch 5/10... Discriminator Loss: 0.8252... Generator Loss: 1.2795
Epoch 5/10... Discriminator Loss: 0.8672... Generator Loss: 1.3544
Epoch 5/10... Discriminator Loss: 0.9494... Generator Loss: 1.9125
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.9642... Generator Loss: 1.0647
Epoch 5/10... Discriminator Loss: 0.7657... Generator Loss: 1.2176
Epoch 5/10... Discriminator Loss: 1.3517... Generator Loss: 0.6631
Epoch 5/10... Discriminator Loss: 0.9540... Generator Loss: 1.5295
Epoch 5/10... Discriminator Loss: 0.9761... Generator Loss: 1.1833
Epoch 5/10... Discriminator Loss: 0.9045... Generator Loss: 1.9913
Epoch 5/10... Discriminator Loss: 0.9037... Generator Loss: 1.2833
Epoch 5/10... Discriminator Loss: 0.7919... Generator Loss: 1.5813
Epoch 5/10... Discriminator Loss: 0.7115... Generator Loss: 2.0273
Epoch 5/10... Discriminator Loss: 0.9957... Generator Loss: 1.3864
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.8249... Generator Loss: 1.5219
Epoch 5/10... Discriminator Loss: 0.8725... Generator Loss: 1.6947
Epoch 5/10... Discriminator Loss: 1.0230... Generator Loss: 1.1683
Epoch 5/10... Discriminator Loss: 1.0003... Generator Loss: 1.1962
Epoch 5/10... Discriminator Loss: 0.6937... Generator Loss: 2.0629
Epoch 5/10... Discriminator Loss: 0.8271... Generator Loss: 2.0919
Epoch 5/10... Discriminator Loss: 0.8583... Generator Loss: 1.5018
Epoch 5/10... Discriminator Loss: 1.0870... Generator Loss: 1.1519
Epoch 5/10... Discriminator Loss: 0.7812... Generator Loss: 2.3407
Epoch 5/10... Discriminator Loss: 1.0420... Generator Loss: 0.8382
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.8152... Generator Loss: 1.3496
Epoch 5/10... Discriminator Loss: 1.0062... Generator Loss: 1.4720
Epoch 5/10... Discriminator Loss: 0.8504... Generator Loss: 1.1142
Epoch 5/10... Discriminator Loss: 1.1152... Generator Loss: 1.0210
Epoch 5/10... Discriminator Loss: 0.7082... Generator Loss: 1.9390
Epoch 5/10... Discriminator Loss: 0.9816... Generator Loss: 1.6163
Epoch 5/10... Discriminator Loss: 0.5504... Generator Loss: 1.9266
Epoch 5/10... Discriminator Loss: 2.0536... Generator Loss: 0.3852
Epoch 5/10... Discriminator Loss: 0.8463... Generator Loss: 1.8357
Epoch 5/10... Discriminator Loss: 0.9615... Generator Loss: 1.3248
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 1.0664... Generator Loss: 1.3476
Epoch 5/10... Discriminator Loss: 0.6510... Generator Loss: 1.4864
Epoch 5/10... Discriminator Loss: 1.0319... Generator Loss: 1.4080
Epoch 5/10... Discriminator Loss: 0.7164... Generator Loss: 1.8030
Epoch 5/10... Discriminator Loss: 1.4368... Generator Loss: 0.6189
Epoch 5/10... Discriminator Loss: 0.9032... Generator Loss: 1.3374
Epoch 5/10... Discriminator Loss: 1.1306... Generator Loss: 1.1527
Epoch 5/10... Discriminator Loss: 1.3456... Generator Loss: 2.2740
Epoch 5/10... Discriminator Loss: 1.3334... Generator Loss: 0.5566
Epoch 5/10... Discriminator Loss: 0.7346... Generator Loss: 2.4016
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.9644... Generator Loss: 1.1082
Epoch 5/10... Discriminator Loss: 0.8788... Generator Loss: 1.5737
Epoch 5/10... Discriminator Loss: 0.9307... Generator Loss: 1.4604
Epoch 5/10... Discriminator Loss: 1.1769... Generator Loss: 1.0245
Epoch 5/10... Discriminator Loss: 0.7521... Generator Loss: 2.1568
Epoch 5/10... Discriminator Loss: 0.6747... Generator Loss: 2.2405
Epoch 5/10... Discriminator Loss: 0.6642... Generator Loss: 1.7710
Epoch 5/10... Discriminator Loss: 0.6684... Generator Loss: 2.3275
Epoch 5/10... Discriminator Loss: 1.0138... Generator Loss: 0.9188
Epoch 5/10... Discriminator Loss: 0.8693... Generator Loss: 1.3336
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.8291... Generator Loss: 1.6587
Epoch 5/10... Discriminator Loss: 1.0400... Generator Loss: 1.9543
Epoch 5/10... Discriminator Loss: 0.9306... Generator Loss: 1.0922
Epoch 5/10... Discriminator Loss: 1.2034... Generator Loss: 1.5089
Epoch 5/10... Discriminator Loss: 0.8584... Generator Loss: 1.2290
Epoch 5/10... Discriminator Loss: 0.7954... Generator Loss: 1.4214
Epoch 5/10... Discriminator Loss: 1.2377... Generator Loss: 0.8107
Epoch 5/10... Discriminator Loss: 0.7714... Generator Loss: 1.8032
Epoch 5/10... Discriminator Loss: 1.5968... Generator Loss: 0.6136
Epoch 5/10... Discriminator Loss: 1.3134... Generator Loss: 1.0223
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 1.0846... Generator Loss: 1.3381
Epoch 5/10... Discriminator Loss: 0.7301... Generator Loss: 1.9210
Epoch 5/10... Discriminator Loss: 1.0001... Generator Loss: 1.5230
Epoch 5/10... Discriminator Loss: 0.9809... Generator Loss: 1.0105
Epoch 5/10... Discriminator Loss: 0.6863... Generator Loss: 1.9188
Epoch 5/10... Discriminator Loss: 0.9751... Generator Loss: 0.9805
Epoch 5/10... Discriminator Loss: 1.2676... Generator Loss: 0.8450
Epoch 5/10... Discriminator Loss: 0.8721... Generator Loss: 1.1732
Epoch 5/10... Discriminator Loss: 1.0387... Generator Loss: 1.5227
Epoch 5/10... Discriminator Loss: 0.7672... Generator Loss: 1.7561
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.8146... Generator Loss: 1.6247
Epoch 5/10... Discriminator Loss: 0.5383... Generator Loss: 2.2931
Epoch 5/10... Discriminator Loss: 0.9158... Generator Loss: 1.2937
Epoch 5/10... Discriminator Loss: 0.9608... Generator Loss: 3.3788
Epoch 5/10... Discriminator Loss: 0.8766... Generator Loss: 1.0521
Epoch 5/10... Discriminator Loss: 0.8263... Generator Loss: 1.3948
Epoch 5/10... Discriminator Loss: 0.6326... Generator Loss: 2.0130
Epoch 5/10... Discriminator Loss: 1.4599... Generator Loss: 0.5112
Epoch 5/10... Discriminator Loss: 0.7583... Generator Loss: 2.3770
Epoch 5/10... Discriminator Loss: 0.8914... Generator Loss: 1.8921
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.6459... Generator Loss: 2.3210
Epoch 5/10... Discriminator Loss: 1.0954... Generator Loss: 1.1723
Epoch 5/10... Discriminator Loss: 0.6734... Generator Loss: 1.7360
Epoch 5/10... Discriminator Loss: 1.0028... Generator Loss: 1.2794
Epoch 5/10... Discriminator Loss: 0.8032... Generator Loss: 1.6740
Epoch 5/10... Discriminator Loss: 0.8994... Generator Loss: 1.4935
Epoch 5/10... Discriminator Loss: 0.7884... Generator Loss: 1.5171
Epoch 5/10... Discriminator Loss: 0.8521... Generator Loss: 0.8636
Epoch 5/10... Discriminator Loss: 0.9761... Generator Loss: 0.9699
Epoch 5/10... Discriminator Loss: 1.2240... Generator Loss: 1.1278
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.8887... Generator Loss: 1.4186
Epoch 5/10... Discriminator Loss: 0.6763... Generator Loss: 1.9324
Epoch 5/10... Discriminator Loss: 1.0202... Generator Loss: 1.8436
Epoch 5/10... Discriminator Loss: 0.7626... Generator Loss: 2.1649
Epoch 5/10... Discriminator Loss: 0.9318... Generator Loss: 1.7890
Epoch 5/10... Discriminator Loss: 0.8589... Generator Loss: 1.7485
Epoch 5/10... Discriminator Loss: 0.9002... Generator Loss: 1.7908
Epoch 5/10... Discriminator Loss: 0.7330... Generator Loss: 1.9755
Epoch 5/10... Discriminator Loss: 0.7740... Generator Loss: 2.2973
Epoch 5/10... Discriminator Loss: 0.8818... Generator Loss: 1.3364
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.7903... Generator Loss: 1.1960
Epoch 5/10... Discriminator Loss: 0.8302... Generator Loss: 1.8902
Epoch 5/10... Discriminator Loss: 0.8946... Generator Loss: 1.2050
Epoch 5/10... Discriminator Loss: 0.7848... Generator Loss: 1.1787
Epoch 5/10... Discriminator Loss: 1.0757... Generator Loss: 1.4388
Epoch 5/10... Discriminator Loss: 0.6871... Generator Loss: 1.7577
Epoch 5/10... Discriminator Loss: 0.7209... Generator Loss: 1.6963
Epoch 5/10... Discriminator Loss: 0.9129... Generator Loss: 1.1162
Epoch 5/10... Discriminator Loss: 1.8276... Generator Loss: 2.2242
Epoch 5/10... Discriminator Loss: 1.2266... Generator Loss: 0.8535
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.6378... Generator Loss: 1.7496
Epoch 5/10... Discriminator Loss: 0.9245... Generator Loss: 2.0502
Epoch 5/10... Discriminator Loss: 0.7944... Generator Loss: 1.4319
Epoch 5/10... Discriminator Loss: 0.6511... Generator Loss: 1.7806
Epoch 5/10... Discriminator Loss: 1.8064... Generator Loss: 0.4063
Epoch 5/10... Discriminator Loss: 0.7008... Generator Loss: 1.6684
Epoch 5/10... Discriminator Loss: 0.6734... Generator Loss: 1.6632
Epoch 5/10... Discriminator Loss: 0.7665... Generator Loss: 2.5146
Epoch 5/10... Discriminator Loss: 0.8676... Generator Loss: 1.3529
Epoch 5/10... Discriminator Loss: 1.1263... Generator Loss: 0.9157
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.5876... Generator Loss: 1.7257
Epoch 5/10... Discriminator Loss: 1.3351... Generator Loss: 0.7692
Epoch 5/10... Discriminator Loss: 1.2416... Generator Loss: 0.6967
Epoch 5/10... Discriminator Loss: 1.4136... Generator Loss: 0.5945
Epoch 5/10... Discriminator Loss: 0.7699... Generator Loss: 1.1890
Epoch 5/10... Discriminator Loss: 1.1527... Generator Loss: 0.9621
Epoch 5/10... Discriminator Loss: 0.8759... Generator Loss: 0.9192
Epoch 5/10... Discriminator Loss: 0.7269... Generator Loss: 1.9454
Epoch 5/10... Discriminator Loss: 0.8299... Generator Loss: 1.7331
Epoch 5/10... Discriminator Loss: 0.8496... Generator Loss: 1.9052
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.5863... Generator Loss: 1.7231
Epoch 5/10... Discriminator Loss: 0.7453... Generator Loss: 1.5751
Epoch 5/10... Discriminator Loss: 0.7714... Generator Loss: 1.3999
Epoch 5/10... Discriminator Loss: 0.8130... Generator Loss: 0.9732
Epoch 5/10... Discriminator Loss: 1.0380... Generator Loss: 1.1985
Epoch 5/10... Discriminator Loss: 1.1129... Generator Loss: 1.0569
Epoch 5/10... Discriminator Loss: 0.7059... Generator Loss: 2.4857
Epoch 5/10... Discriminator Loss: 0.8376... Generator Loss: 1.4468
Epoch 5/10... Discriminator Loss: 1.0116... Generator Loss: 1.4830
Epoch 5/10... Discriminator Loss: 1.2733... Generator Loss: 1.1264
<class 'numpy.ndarray'>
Epoch 5/10... Discriminator Loss: 0.9468... Generator Loss: 1.9402
Epoch 6/10... Discriminator Loss: 0.7867... Generator Loss: 2.0128
Epoch 6/10... Discriminator Loss: 0.8243... Generator Loss: 1.5817
Epoch 6/10... Discriminator Loss: 0.7908... Generator Loss: 1.5842
Epoch 6/10... Discriminator Loss: 1.1453... Generator Loss: 1.1372
Epoch 6/10... Discriminator Loss: 0.7566... Generator Loss: 1.1975
Epoch 6/10... Discriminator Loss: 0.7895... Generator Loss: 1.4109
Epoch 6/10... Discriminator Loss: 1.1459... Generator Loss: 1.0434
Epoch 6/10... Discriminator Loss: 0.5956... Generator Loss: 1.6021
Epoch 6/10... Discriminator Loss: 1.2226... Generator Loss: 1.0601
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 1.1623... Generator Loss: 0.8473
Epoch 6/10... Discriminator Loss: 0.7273... Generator Loss: 2.0439
Epoch 6/10... Discriminator Loss: 1.1098... Generator Loss: 0.9282
Epoch 6/10... Discriminator Loss: 0.8414... Generator Loss: 1.6209
Epoch 6/10... Discriminator Loss: 1.3681... Generator Loss: 0.6040
Epoch 6/10... Discriminator Loss: 0.8426... Generator Loss: 2.2402
Epoch 6/10... Discriminator Loss: 1.2804... Generator Loss: 0.8046
Epoch 6/10... Discriminator Loss: 0.9360... Generator Loss: 0.9797
Epoch 6/10... Discriminator Loss: 0.6492... Generator Loss: 2.0838
Epoch 6/10... Discriminator Loss: 0.6691... Generator Loss: 1.6938
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.8110... Generator Loss: 3.3034
Epoch 6/10... Discriminator Loss: 0.9178... Generator Loss: 1.2960
Epoch 6/10... Discriminator Loss: 0.8227... Generator Loss: 1.9103
Epoch 6/10... Discriminator Loss: 0.9502... Generator Loss: 2.4429
Epoch 6/10... Discriminator Loss: 0.7805... Generator Loss: 1.4032
Epoch 6/10... Discriminator Loss: 1.0725... Generator Loss: 1.2676
Epoch 6/10... Discriminator Loss: 0.8305... Generator Loss: 1.5963
Epoch 6/10... Discriminator Loss: 0.6977... Generator Loss: 1.9190
Epoch 6/10... Discriminator Loss: 0.7918... Generator Loss: 2.5657
Epoch 6/10... Discriminator Loss: 1.4513... Generator Loss: 0.4766
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.7315... Generator Loss: 2.2480
Epoch 6/10... Discriminator Loss: 0.6911... Generator Loss: 1.7110
Epoch 6/10... Discriminator Loss: 1.0407... Generator Loss: 1.0905
Epoch 6/10... Discriminator Loss: 0.7033... Generator Loss: 1.2993
Epoch 6/10... Discriminator Loss: 0.5975... Generator Loss: 2.8952
Epoch 6/10... Discriminator Loss: 0.9675... Generator Loss: 2.2101
Epoch 6/10... Discriminator Loss: 0.9294... Generator Loss: 2.2714
Epoch 6/10... Discriminator Loss: 0.9109... Generator Loss: 1.4788
Epoch 6/10... Discriminator Loss: 0.9945... Generator Loss: 1.1725
Epoch 6/10... Discriminator Loss: 0.9609... Generator Loss: 0.8291
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.9385... Generator Loss: 1.4859
Epoch 6/10... Discriminator Loss: 0.8684... Generator Loss: 1.6542
Epoch 6/10... Discriminator Loss: 0.5837... Generator Loss: 2.9706
Epoch 6/10... Discriminator Loss: 0.8567... Generator Loss: 1.8056
Epoch 6/10... Discriminator Loss: 1.4386... Generator Loss: 1.7147
Epoch 6/10... Discriminator Loss: 0.5507... Generator Loss: 2.8829
Epoch 6/10... Discriminator Loss: 1.2911... Generator Loss: 0.8033
Epoch 6/10... Discriminator Loss: 0.6351... Generator Loss: 2.5295
Epoch 6/10... Discriminator Loss: 0.6149... Generator Loss: 2.0354
Epoch 6/10... Discriminator Loss: 0.7807... Generator Loss: 2.4812
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.8694... Generator Loss: 1.2119
Epoch 6/10... Discriminator Loss: 0.9232... Generator Loss: 1.4481
Epoch 6/10... Discriminator Loss: 0.6207... Generator Loss: 2.5498
Epoch 6/10... Discriminator Loss: 0.7153... Generator Loss: 2.4419
Epoch 6/10... Discriminator Loss: 0.7261... Generator Loss: 1.6285
Epoch 6/10... Discriminator Loss: 1.5414... Generator Loss: 1.3428
Epoch 6/10... Discriminator Loss: 1.4558... Generator Loss: 0.5888
Epoch 6/10... Discriminator Loss: 1.0142... Generator Loss: 1.5829
Epoch 6/10... Discriminator Loss: 0.7809... Generator Loss: 2.1088
Epoch 6/10... Discriminator Loss: 1.1129... Generator Loss: 0.6991
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.5853... Generator Loss: 2.4275
Epoch 6/10... Discriminator Loss: 0.7211... Generator Loss: 1.6653
Epoch 6/10... Discriminator Loss: 0.7492... Generator Loss: 1.7838
Epoch 6/10... Discriminator Loss: 0.7840... Generator Loss: 1.3673
Epoch 6/10... Discriminator Loss: 0.7289... Generator Loss: 1.3895
Epoch 6/10... Discriminator Loss: 1.3222... Generator Loss: 0.6776
Epoch 6/10... Discriminator Loss: 1.1518... Generator Loss: 0.9253
Epoch 6/10... Discriminator Loss: 0.8716... Generator Loss: 0.9420
Epoch 6/10... Discriminator Loss: 1.1429... Generator Loss: 0.9107
Epoch 6/10... Discriminator Loss: 1.0753... Generator Loss: 1.1836
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.6812... Generator Loss: 2.7417
Epoch 6/10... Discriminator Loss: 1.2639... Generator Loss: 0.7517
Epoch 6/10... Discriminator Loss: 0.9807... Generator Loss: 1.7152
Epoch 6/10... Discriminator Loss: 0.6853... Generator Loss: 1.4534
Epoch 6/10... Discriminator Loss: 0.6728... Generator Loss: 2.0122
Epoch 6/10... Discriminator Loss: 0.6033... Generator Loss: 2.0078
Epoch 6/10... Discriminator Loss: 0.8668... Generator Loss: 2.0483
Epoch 6/10... Discriminator Loss: 0.6399... Generator Loss: 2.2717
Epoch 6/10... Discriminator Loss: 1.0807... Generator Loss: 1.2403
Epoch 6/10... Discriminator Loss: 0.8931... Generator Loss: 2.0351
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.7563... Generator Loss: 1.8003
Epoch 6/10... Discriminator Loss: 0.7663... Generator Loss: 1.4980
Epoch 6/10... Discriminator Loss: 1.5677... Generator Loss: 0.5821
Epoch 6/10... Discriminator Loss: 0.5727... Generator Loss: 2.6005
Epoch 6/10... Discriminator Loss: 0.9249... Generator Loss: 1.4891
Epoch 6/10... Discriminator Loss: 0.8139... Generator Loss: 1.9391
Epoch 6/10... Discriminator Loss: 0.8170... Generator Loss: 1.8456
Epoch 6/10... Discriminator Loss: 1.0713... Generator Loss: 0.7244
Epoch 6/10... Discriminator Loss: 0.9559... Generator Loss: 1.1022
Epoch 6/10... Discriminator Loss: 0.6780... Generator Loss: 1.2565
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.9077... Generator Loss: 1.2032
Epoch 6/10... Discriminator Loss: 1.1367... Generator Loss: 1.4897
Epoch 6/10... Discriminator Loss: 0.6227... Generator Loss: 2.3274
Epoch 6/10... Discriminator Loss: 1.4923... Generator Loss: 0.4985
Epoch 6/10... Discriminator Loss: 0.6876... Generator Loss: 2.3048
Epoch 6/10... Discriminator Loss: 0.8311... Generator Loss: 1.4869
Epoch 6/10... Discriminator Loss: 0.7806... Generator Loss: 1.5415
Epoch 6/10... Discriminator Loss: 0.6905... Generator Loss: 1.8474
Epoch 6/10... Discriminator Loss: 0.9762... Generator Loss: 1.1756
Epoch 6/10... Discriminator Loss: 0.8965... Generator Loss: 1.8652
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.7046... Generator Loss: 1.9434
Epoch 6/10... Discriminator Loss: 0.7986... Generator Loss: 2.9160
Epoch 6/10... Discriminator Loss: 0.6113... Generator Loss: 1.8953
Epoch 6/10... Discriminator Loss: 0.5929... Generator Loss: 2.4862
Epoch 6/10... Discriminator Loss: 0.7859... Generator Loss: 3.2053
Epoch 6/10... Discriminator Loss: 0.7816... Generator Loss: 1.3522
Epoch 6/10... Discriminator Loss: 0.7052... Generator Loss: 1.9586
Epoch 6/10... Discriminator Loss: 1.1471... Generator Loss: 1.2965
Epoch 6/10... Discriminator Loss: 0.8621... Generator Loss: 2.1892
Epoch 6/10... Discriminator Loss: 0.9201... Generator Loss: 1.1650
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.7391... Generator Loss: 1.5402
Epoch 6/10... Discriminator Loss: 1.8345... Generator Loss: 0.3619
Epoch 6/10... Discriminator Loss: 1.1106... Generator Loss: 0.8994
Epoch 6/10... Discriminator Loss: 0.6902... Generator Loss: 1.6755
Epoch 6/10... Discriminator Loss: 1.0371... Generator Loss: 1.4308
Epoch 6/10... Discriminator Loss: 1.1274... Generator Loss: 1.1970
Epoch 6/10... Discriminator Loss: 0.7376... Generator Loss: 1.2308
Epoch 6/10... Discriminator Loss: 0.6618... Generator Loss: 2.1210
Epoch 6/10... Discriminator Loss: 0.7872... Generator Loss: 0.9154
Epoch 6/10... Discriminator Loss: 1.0471... Generator Loss: 1.1432
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.5757... Generator Loss: 1.9290
Epoch 6/10... Discriminator Loss: 0.7276... Generator Loss: 1.6091
Epoch 6/10... Discriminator Loss: 0.6335... Generator Loss: 2.3471
Epoch 6/10... Discriminator Loss: 0.6359... Generator Loss: 1.5645
Epoch 6/10... Discriminator Loss: 0.7188... Generator Loss: 1.8003
Epoch 6/10... Discriminator Loss: 0.6344... Generator Loss: 2.5052
Epoch 6/10... Discriminator Loss: 0.8334... Generator Loss: 1.6560
Epoch 6/10... Discriminator Loss: 1.3009... Generator Loss: 1.3380
Epoch 6/10... Discriminator Loss: 0.9720... Generator Loss: 1.4547
Epoch 6/10... Discriminator Loss: 1.1780... Generator Loss: 0.9049
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 1.3117... Generator Loss: 0.5001
Epoch 6/10... Discriminator Loss: 0.8609... Generator Loss: 1.3712
Epoch 6/10... Discriminator Loss: 0.7663... Generator Loss: 1.8333
Epoch 6/10... Discriminator Loss: 0.8629... Generator Loss: 1.6886
Epoch 6/10... Discriminator Loss: 0.9757... Generator Loss: 0.9610
Epoch 6/10... Discriminator Loss: 0.5061... Generator Loss: 2.6353
Epoch 6/10... Discriminator Loss: 0.6091... Generator Loss: 3.0188
Epoch 6/10... Discriminator Loss: 1.0110... Generator Loss: 0.8021
Epoch 6/10... Discriminator Loss: 0.5389... Generator Loss: 2.1404
Epoch 6/10... Discriminator Loss: 0.6211... Generator Loss: 2.2681
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.8455... Generator Loss: 0.9720
Epoch 6/10... Discriminator Loss: 1.3201... Generator Loss: 0.6340
Epoch 6/10... Discriminator Loss: 0.7929... Generator Loss: 1.3079
Epoch 6/10... Discriminator Loss: 0.9874... Generator Loss: 0.9689
Epoch 6/10... Discriminator Loss: 1.1637... Generator Loss: 1.2092
Epoch 6/10... Discriminator Loss: 0.9414... Generator Loss: 1.9478
Epoch 6/10... Discriminator Loss: 1.1699... Generator Loss: 0.8997
Epoch 6/10... Discriminator Loss: 0.7322... Generator Loss: 1.9078
Epoch 6/10... Discriminator Loss: 0.6288... Generator Loss: 1.2958
Epoch 6/10... Discriminator Loss: 0.8377... Generator Loss: 1.6880
<class 'numpy.ndarray'>
Epoch 6/10... Discriminator Loss: 0.9356... Generator Loss: 1.9874
Epoch 6/10... Discriminator Loss: 0.6700... Generator Loss: 1.6271
Epoch 6/10... Discriminator Loss: 0.5824... Generator Loss: 1.6973
Epoch 6/10... Discriminator Loss: 1.1640... Generator Loss: 1.2682
Epoch 6/10... Discriminator Loss: 1.1258... Generator Loss: 1.0864
Epoch 6/10... Discriminator Loss: 0.6767... Generator Loss: 1.8349
Epoch 6/10... Discriminator Loss: 0.4626... Generator Loss: 2.9602
Epoch 6/10... Discriminator Loss: 0.6838... Generator Loss: 1.8754
Epoch 6/10... Discriminator Loss: 0.9951... Generator Loss: 1.4371
Epoch 7/10... Discriminator Loss: 0.6962... Generator Loss: 1.5805
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.5443... Generator Loss: 2.2885
Epoch 7/10... Discriminator Loss: 1.6092... Generator Loss: 0.3642
Epoch 7/10... Discriminator Loss: 0.7300... Generator Loss: 1.7195
Epoch 7/10... Discriminator Loss: 1.0527... Generator Loss: 0.8412
Epoch 7/10... Discriminator Loss: 1.0475... Generator Loss: 1.2534
Epoch 7/10... Discriminator Loss: 1.3681... Generator Loss: 0.7451
Epoch 7/10... Discriminator Loss: 0.5426... Generator Loss: 2.6607
Epoch 7/10... Discriminator Loss: 1.1161... Generator Loss: 0.9734
Epoch 7/10... Discriminator Loss: 0.6340... Generator Loss: 2.5786
Epoch 7/10... Discriminator Loss: 0.8651... Generator Loss: 1.7000
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 1.1178... Generator Loss: 4.3654
Epoch 7/10... Discriminator Loss: 0.5852... Generator Loss: 2.3398
Epoch 7/10... Discriminator Loss: 1.1593... Generator Loss: 2.2025
Epoch 7/10... Discriminator Loss: 0.8383... Generator Loss: 1.5362
Epoch 7/10... Discriminator Loss: 0.9964... Generator Loss: 0.8625
Epoch 7/10... Discriminator Loss: 0.6722... Generator Loss: 1.5916
Epoch 7/10... Discriminator Loss: 0.7655... Generator Loss: 1.9247
Epoch 7/10... Discriminator Loss: 0.6805... Generator Loss: 1.3130
Epoch 7/10... Discriminator Loss: 0.8035... Generator Loss: 2.0702
Epoch 7/10... Discriminator Loss: 1.0283... Generator Loss: 1.4687
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.7528... Generator Loss: 4.4249
Epoch 7/10... Discriminator Loss: 0.8669... Generator Loss: 1.9264
Epoch 7/10... Discriminator Loss: 0.6455... Generator Loss: 2.2068
Epoch 7/10... Discriminator Loss: 0.7674... Generator Loss: 1.5445
Epoch 7/10... Discriminator Loss: 0.6067... Generator Loss: 2.2819
Epoch 7/10... Discriminator Loss: 0.9987... Generator Loss: 0.8389
Epoch 7/10... Discriminator Loss: 0.9868... Generator Loss: 1.0135
Epoch 7/10... Discriminator Loss: 0.7272... Generator Loss: 1.8835
Epoch 7/10... Discriminator Loss: 0.6814... Generator Loss: 2.0506
Epoch 7/10... Discriminator Loss: 0.7273... Generator Loss: 2.1422
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.9535... Generator Loss: 1.1624
Epoch 7/10... Discriminator Loss: 0.7449... Generator Loss: 1.3350
Epoch 7/10... Discriminator Loss: 0.5651... Generator Loss: 2.0851
Epoch 7/10... Discriminator Loss: 0.6965... Generator Loss: 1.9570
Epoch 7/10... Discriminator Loss: 0.9532... Generator Loss: 1.9569
Epoch 7/10... Discriminator Loss: 0.5389... Generator Loss: 2.5305
Epoch 7/10... Discriminator Loss: 0.7858... Generator Loss: 1.2086
Epoch 7/10... Discriminator Loss: 0.7179... Generator Loss: 2.2929
Epoch 7/10... Discriminator Loss: 0.5876... Generator Loss: 3.9633
Epoch 7/10... Discriminator Loss: 0.5748... Generator Loss: 2.3556
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.6452... Generator Loss: 1.8127
Epoch 7/10... Discriminator Loss: 0.6110... Generator Loss: 3.0359
Epoch 7/10... Discriminator Loss: 0.6030... Generator Loss: 2.9354
Epoch 7/10... Discriminator Loss: 0.5667... Generator Loss: 1.2604
Epoch 7/10... Discriminator Loss: 0.4737... Generator Loss: 3.4205
Epoch 7/10... Discriminator Loss: 0.7925... Generator Loss: 1.5073
Epoch 7/10... Discriminator Loss: 1.0423... Generator Loss: 0.9550
Epoch 7/10... Discriminator Loss: 1.0209... Generator Loss: 2.1516
Epoch 7/10... Discriminator Loss: 0.6060... Generator Loss: 2.4671
Epoch 7/10... Discriminator Loss: 0.8889... Generator Loss: 1.6958
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.5936... Generator Loss: 2.4370
Epoch 7/10... Discriminator Loss: 0.8426... Generator Loss: 1.9661
Epoch 7/10... Discriminator Loss: 0.7299... Generator Loss: 3.1201
Epoch 7/10... Discriminator Loss: 1.1369... Generator Loss: 0.8571
Epoch 7/10... Discriminator Loss: 0.9402... Generator Loss: 1.1501
Epoch 7/10... Discriminator Loss: 1.0112... Generator Loss: 0.5645
Epoch 7/10... Discriminator Loss: 0.8299... Generator Loss: 1.8209
Epoch 7/10... Discriminator Loss: 0.7246... Generator Loss: 1.5854
Epoch 7/10... Discriminator Loss: 0.5418... Generator Loss: 2.4095
Epoch 7/10... Discriminator Loss: 0.5092... Generator Loss: 3.0050
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.6762... Generator Loss: 1.5721
Epoch 7/10... Discriminator Loss: 1.1705... Generator Loss: 1.2181
Epoch 7/10... Discriminator Loss: 0.6587... Generator Loss: 2.2032
Epoch 7/10... Discriminator Loss: 0.7019... Generator Loss: 1.6116
Epoch 7/10... Discriminator Loss: 0.6339... Generator Loss: 1.4206
Epoch 7/10... Discriminator Loss: 0.6575... Generator Loss: 2.8184
Epoch 7/10... Discriminator Loss: 0.6215... Generator Loss: 2.6687
Epoch 7/10... Discriminator Loss: 0.6490... Generator Loss: 2.4582
Epoch 7/10... Discriminator Loss: 0.7392... Generator Loss: 1.9240
Epoch 7/10... Discriminator Loss: 0.5571... Generator Loss: 2.4794
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.9535... Generator Loss: 1.5713
Epoch 7/10... Discriminator Loss: 1.9067... Generator Loss: 0.4342
Epoch 7/10... Discriminator Loss: 0.9283... Generator Loss: 2.3082
Epoch 7/10... Discriminator Loss: 0.8748... Generator Loss: 1.3820
Epoch 7/10... Discriminator Loss: 1.0257... Generator Loss: 0.8999
Epoch 7/10... Discriminator Loss: 1.0770... Generator Loss: 0.9641
Epoch 7/10... Discriminator Loss: 0.9281... Generator Loss: 1.0112
Epoch 7/10... Discriminator Loss: 1.4581... Generator Loss: 0.6083
Epoch 7/10... Discriminator Loss: 0.4585... Generator Loss: 2.0319
Epoch 7/10... Discriminator Loss: 1.2152... Generator Loss: 0.9841
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 1.1803... Generator Loss: 2.5799
Epoch 7/10... Discriminator Loss: 0.7277... Generator Loss: 1.6527
Epoch 7/10... Discriminator Loss: 0.9185... Generator Loss: 1.7928
Epoch 7/10... Discriminator Loss: 0.9433... Generator Loss: 1.4664
Epoch 7/10... Discriminator Loss: 0.6714... Generator Loss: 2.1012
Epoch 7/10... Discriminator Loss: 0.8374... Generator Loss: 2.6699
Epoch 7/10... Discriminator Loss: 0.9206... Generator Loss: 2.2935
Epoch 7/10... Discriminator Loss: 0.6155... Generator Loss: 2.7368
Epoch 7/10... Discriminator Loss: 0.5866... Generator Loss: 2.4981
Epoch 7/10... Discriminator Loss: 0.6159... Generator Loss: 2.0531
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.5453... Generator Loss: 1.8317
Epoch 7/10... Discriminator Loss: 0.7401... Generator Loss: 1.6895
Epoch 7/10... Discriminator Loss: 0.9762... Generator Loss: 0.8000
Epoch 7/10... Discriminator Loss: 0.5773... Generator Loss: 2.2820
Epoch 7/10... Discriminator Loss: 0.7969... Generator Loss: 1.7553
Epoch 7/10... Discriminator Loss: 0.6724... Generator Loss: 1.8110
Epoch 7/10... Discriminator Loss: 0.9534... Generator Loss: 2.6650
Epoch 7/10... Discriminator Loss: 1.0278... Generator Loss: 1.1508
Epoch 7/10... Discriminator Loss: 1.0457... Generator Loss: 0.6883
Epoch 7/10... Discriminator Loss: 0.6302... Generator Loss: 1.3809
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.7348... Generator Loss: 1.9330
Epoch 7/10... Discriminator Loss: 0.9378... Generator Loss: 1.3513
Epoch 7/10... Discriminator Loss: 0.7973... Generator Loss: 0.9662
Epoch 7/10... Discriminator Loss: 0.6169... Generator Loss: 2.3139
Epoch 7/10... Discriminator Loss: 0.7386... Generator Loss: 2.3275
Epoch 7/10... Discriminator Loss: 0.6606... Generator Loss: 2.5048
Epoch 7/10... Discriminator Loss: 0.8485... Generator Loss: 1.8378
Epoch 7/10... Discriminator Loss: 0.7980... Generator Loss: 2.3353
Epoch 7/10... Discriminator Loss: 0.7595... Generator Loss: 2.4334
Epoch 7/10... Discriminator Loss: 0.7291... Generator Loss: 2.0105
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.6909... Generator Loss: 2.4452
Epoch 7/10... Discriminator Loss: 0.5117... Generator Loss: 2.0808
Epoch 7/10... Discriminator Loss: 0.6011... Generator Loss: 2.6855
Epoch 7/10... Discriminator Loss: 0.4871... Generator Loss: 2.5657
Epoch 7/10... Discriminator Loss: 0.5500... Generator Loss: 2.2500
Epoch 7/10... Discriminator Loss: 0.8180... Generator Loss: 2.1869
Epoch 7/10... Discriminator Loss: 0.6361... Generator Loss: 2.0208
Epoch 7/10... Discriminator Loss: 0.7338... Generator Loss: 2.8340
Epoch 7/10... Discriminator Loss: 0.7201... Generator Loss: 2.8192
Epoch 7/10... Discriminator Loss: 0.7402... Generator Loss: 1.5743
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.6065... Generator Loss: 2.6547
Epoch 7/10... Discriminator Loss: 0.8953... Generator Loss: 2.0948
Epoch 7/10... Discriminator Loss: 0.8251... Generator Loss: 1.5164
Epoch 7/10... Discriminator Loss: 0.6045... Generator Loss: 3.2005
Epoch 7/10... Discriminator Loss: 0.9132... Generator Loss: 1.2106
Epoch 7/10... Discriminator Loss: 0.5262... Generator Loss: 2.9361
Epoch 7/10... Discriminator Loss: 0.7510... Generator Loss: 1.6304
Epoch 7/10... Discriminator Loss: 0.5238... Generator Loss: 2.4686
Epoch 7/10... Discriminator Loss: 0.7078... Generator Loss: 1.9818
Epoch 7/10... Discriminator Loss: 0.7217... Generator Loss: 2.0469
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 1.0625... Generator Loss: 0.9132
Epoch 7/10... Discriminator Loss: 1.7310... Generator Loss: 1.3959
Epoch 7/10... Discriminator Loss: 0.6521... Generator Loss: 1.6986
Epoch 7/10... Discriminator Loss: 0.7378... Generator Loss: 1.3024
Epoch 7/10... Discriminator Loss: 0.6002... Generator Loss: 2.4884
Epoch 7/10... Discriminator Loss: 0.6808... Generator Loss: 1.7399
Epoch 7/10... Discriminator Loss: 0.7321... Generator Loss: 0.9721
Epoch 7/10... Discriminator Loss: 0.6745... Generator Loss: 2.5208
Epoch 7/10... Discriminator Loss: 0.6089... Generator Loss: 2.1788
Epoch 7/10... Discriminator Loss: 0.7416... Generator Loss: 1.7997
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.7197... Generator Loss: 1.7510
Epoch 7/10... Discriminator Loss: 0.7059... Generator Loss: 1.6598
Epoch 7/10... Discriminator Loss: 1.3235... Generator Loss: 0.5340
Epoch 7/10... Discriminator Loss: 0.9473... Generator Loss: 1.5312
Epoch 7/10... Discriminator Loss: 0.6949... Generator Loss: 1.5197
Epoch 7/10... Discriminator Loss: 1.1528... Generator Loss: 1.2699
Epoch 7/10... Discriminator Loss: 1.2280... Generator Loss: 0.5488
Epoch 7/10... Discriminator Loss: 0.9572... Generator Loss: 1.3310
Epoch 7/10... Discriminator Loss: 0.5508... Generator Loss: 1.6153
Epoch 7/10... Discriminator Loss: 0.6702... Generator Loss: 1.8384
<class 'numpy.ndarray'>
Epoch 7/10... Discriminator Loss: 0.5816... Generator Loss: 2.7593
Epoch 7/10... Discriminator Loss: 0.6442... Generator Loss: 1.6129
Epoch 7/10... Discriminator Loss: 0.5635... Generator Loss: 2.5532
Epoch 7/10... Discriminator Loss: 0.9275... Generator Loss: 1.2860
Epoch 7/10... Discriminator Loss: 0.4821... Generator Loss: 3.0964
Epoch 7/10... Discriminator Loss: 0.7001... Generator Loss: 1.2992
Epoch 7/10... Discriminator Loss: 0.5490... Generator Loss: 2.7923
Epoch 8/10... Discriminator Loss: 0.5801... Generator Loss: 1.4633
Epoch 8/10... Discriminator Loss: 0.6231... Generator Loss: 1.7045
Epoch 8/10... Discriminator Loss: 0.5115... Generator Loss: 2.8851
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4243... Generator Loss: 2.8060
Epoch 8/10... Discriminator Loss: 0.5790... Generator Loss: 1.9894
Epoch 8/10... Discriminator Loss: 0.8875... Generator Loss: 1.3383
Epoch 8/10... Discriminator Loss: 0.6562... Generator Loss: 2.5900
Epoch 8/10... Discriminator Loss: 0.8091... Generator Loss: 2.1806
Epoch 8/10... Discriminator Loss: 0.9808... Generator Loss: 1.4429
Epoch 8/10... Discriminator Loss: 0.7175... Generator Loss: 1.6390
Epoch 8/10... Discriminator Loss: 0.7902... Generator Loss: 1.5942
Epoch 8/10... Discriminator Loss: 0.5060... Generator Loss: 2.7844
Epoch 8/10... Discriminator Loss: 0.6278... Generator Loss: 2.5735
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.7830... Generator Loss: 1.4814
Epoch 8/10... Discriminator Loss: 0.5869... Generator Loss: 1.9683
Epoch 8/10... Discriminator Loss: 0.6171... Generator Loss: 2.8484
Epoch 8/10... Discriminator Loss: 0.5567... Generator Loss: 1.8820
Epoch 8/10... Discriminator Loss: 0.5912... Generator Loss: 1.9961
Epoch 8/10... Discriminator Loss: 0.8792... Generator Loss: 1.3635
Epoch 8/10... Discriminator Loss: 1.1899... Generator Loss: 0.5678
Epoch 8/10... Discriminator Loss: 0.9102... Generator Loss: 1.1382
Epoch 8/10... Discriminator Loss: 1.0425... Generator Loss: 0.9664
Epoch 8/10... Discriminator Loss: 1.2770... Generator Loss: 0.6365
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4931... Generator Loss: 2.1306
Epoch 8/10... Discriminator Loss: 0.8814... Generator Loss: 1.3534
Epoch 8/10... Discriminator Loss: 0.6280... Generator Loss: 2.0323
Epoch 8/10... Discriminator Loss: 0.5179... Generator Loss: 2.5566
Epoch 8/10... Discriminator Loss: 0.7022... Generator Loss: 3.2014
Epoch 8/10... Discriminator Loss: 0.6778... Generator Loss: 1.2376
Epoch 8/10... Discriminator Loss: 0.6090... Generator Loss: 2.0503
Epoch 8/10... Discriminator Loss: 1.0335... Generator Loss: 2.9406
Epoch 8/10... Discriminator Loss: 0.4221... Generator Loss: 3.6744
Epoch 8/10... Discriminator Loss: 0.7612... Generator Loss: 3.4113
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.6716... Generator Loss: 1.8816
Epoch 8/10... Discriminator Loss: 0.9499... Generator Loss: 1.7340
Epoch 8/10... Discriminator Loss: 0.6896... Generator Loss: 2.5724
Epoch 8/10... Discriminator Loss: 0.6631... Generator Loss: 1.7018
Epoch 8/10... Discriminator Loss: 1.1496... Generator Loss: 0.7235
Epoch 8/10... Discriminator Loss: 0.6834... Generator Loss: 1.2126
Epoch 8/10... Discriminator Loss: 0.9078... Generator Loss: 0.5553
Epoch 8/10... Discriminator Loss: 0.7521... Generator Loss: 1.7437
Epoch 8/10... Discriminator Loss: 0.7810... Generator Loss: 2.1270
Epoch 8/10... Discriminator Loss: 0.5126... Generator Loss: 2.5091
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.8094... Generator Loss: 1.4183
Epoch 8/10... Discriminator Loss: 0.6532... Generator Loss: 2.5987
Epoch 8/10... Discriminator Loss: 1.4768... Generator Loss: 2.1650
Epoch 8/10... Discriminator Loss: 0.6727... Generator Loss: 2.2589
Epoch 8/10... Discriminator Loss: 0.6410... Generator Loss: 2.9647
Epoch 8/10... Discriminator Loss: 0.6070... Generator Loss: 1.8063
Epoch 8/10... Discriminator Loss: 0.8277... Generator Loss: 3.8850
Epoch 8/10... Discriminator Loss: 0.6909... Generator Loss: 2.9725
Epoch 8/10... Discriminator Loss: 0.7556... Generator Loss: 1.1344
Epoch 8/10... Discriminator Loss: 0.6894... Generator Loss: 1.9413
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.6549... Generator Loss: 1.4979
Epoch 8/10... Discriminator Loss: 0.5402... Generator Loss: 2.2038
Epoch 8/10... Discriminator Loss: 0.5063... Generator Loss: 2.8811
Epoch 8/10... Discriminator Loss: 0.4967... Generator Loss: 2.5258
Epoch 8/10... Discriminator Loss: 0.5696... Generator Loss: 1.5826
Epoch 8/10... Discriminator Loss: 0.7401... Generator Loss: 1.9918
Epoch 8/10... Discriminator Loss: 0.6600... Generator Loss: 2.0908
Epoch 8/10... Discriminator Loss: 0.4917... Generator Loss: 2.3948
Epoch 8/10... Discriminator Loss: 0.9130... Generator Loss: 0.9761
Epoch 8/10... Discriminator Loss: 0.6196... Generator Loss: 2.1626
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.7107... Generator Loss: 2.9648
Epoch 8/10... Discriminator Loss: 0.4446... Generator Loss: 2.6977
Epoch 8/10... Discriminator Loss: 0.9116... Generator Loss: 1.5747
Epoch 8/10... Discriminator Loss: 0.7122... Generator Loss: 1.4317
Epoch 8/10... Discriminator Loss: 0.7306... Generator Loss: 1.7998
Epoch 8/10... Discriminator Loss: 0.6715... Generator Loss: 3.3180
Epoch 8/10... Discriminator Loss: 0.5028... Generator Loss: 3.5845
Epoch 8/10... Discriminator Loss: 0.6235... Generator Loss: 2.3348
Epoch 8/10... Discriminator Loss: 0.7032... Generator Loss: 1.7555
Epoch 8/10... Discriminator Loss: 0.6632... Generator Loss: 1.8652
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 1.2161... Generator Loss: 0.6802
Epoch 8/10... Discriminator Loss: 0.7668... Generator Loss: 1.6491
Epoch 8/10... Discriminator Loss: 0.6428... Generator Loss: 1.9288
Epoch 8/10... Discriminator Loss: 0.8227... Generator Loss: 1.8540
Epoch 8/10... Discriminator Loss: 0.5771... Generator Loss: 3.6530
Epoch 8/10... Discriminator Loss: 0.6304... Generator Loss: 2.1653
Epoch 8/10... Discriminator Loss: 1.0219... Generator Loss: 0.8893
Epoch 8/10... Discriminator Loss: 0.6890... Generator Loss: 2.0404
Epoch 8/10... Discriminator Loss: 1.3109... Generator Loss: 1.1958
Epoch 8/10... Discriminator Loss: 0.9687... Generator Loss: 1.0729
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.7552... Generator Loss: 2.3576
Epoch 8/10... Discriminator Loss: 0.5925... Generator Loss: 1.9327
Epoch 8/10... Discriminator Loss: 1.0637... Generator Loss: 1.3666
Epoch 8/10... Discriminator Loss: 0.6382... Generator Loss: 1.5198
Epoch 8/10... Discriminator Loss: 0.8634... Generator Loss: 1.5222
Epoch 8/10... Discriminator Loss: 0.7638... Generator Loss: 1.5469
Epoch 8/10... Discriminator Loss: 0.8294... Generator Loss: 2.3921
Epoch 8/10... Discriminator Loss: 0.7710... Generator Loss: 2.8945
Epoch 8/10... Discriminator Loss: 0.5762... Generator Loss: 3.3549
Epoch 8/10... Discriminator Loss: 0.4742... Generator Loss: 2.6807
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4634... Generator Loss: 2.8954
Epoch 8/10... Discriminator Loss: 0.9856... Generator Loss: 1.4034
Epoch 8/10... Discriminator Loss: 0.8649... Generator Loss: 1.5740
Epoch 8/10... Discriminator Loss: 0.4969... Generator Loss: 2.5151
Epoch 8/10... Discriminator Loss: 1.2187... Generator Loss: 1.2851
Epoch 8/10... Discriminator Loss: 0.6737... Generator Loss: 2.4959
Epoch 8/10... Discriminator Loss: 0.8854... Generator Loss: 1.6386
Epoch 8/10... Discriminator Loss: 0.6614... Generator Loss: 1.6001
Epoch 8/10... Discriminator Loss: 0.4963... Generator Loss: 3.4618
Epoch 8/10... Discriminator Loss: 0.6053... Generator Loss: 1.2620
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.4621... Generator Loss: 2.8670
Epoch 8/10... Discriminator Loss: 0.4971... Generator Loss: 3.3047
Epoch 8/10... Discriminator Loss: 0.8895... Generator Loss: 1.1435
Epoch 8/10... Discriminator Loss: 0.9993... Generator Loss: 1.3836
Epoch 8/10... Discriminator Loss: 0.5598... Generator Loss: 1.5128
Epoch 8/10... Discriminator Loss: 0.6947... Generator Loss: 1.4312
Epoch 8/10... Discriminator Loss: 0.8910... Generator Loss: 1.2711
Epoch 8/10... Discriminator Loss: 0.4324... Generator Loss: 2.8453
Epoch 8/10... Discriminator Loss: 1.0135... Generator Loss: 1.3585
Epoch 8/10... Discriminator Loss: 0.5079... Generator Loss: 3.2460
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.6476... Generator Loss: 2.3391
Epoch 8/10... Discriminator Loss: 0.7326... Generator Loss: 0.9130
Epoch 8/10... Discriminator Loss: 0.5157... Generator Loss: 3.1560
Epoch 8/10... Discriminator Loss: 0.6983... Generator Loss: 1.7285
Epoch 8/10... Discriminator Loss: 0.6473... Generator Loss: 2.1125
Epoch 8/10... Discriminator Loss: 0.6483... Generator Loss: 3.3515
Epoch 8/10... Discriminator Loss: 0.6741... Generator Loss: 1.5832
Epoch 8/10... Discriminator Loss: 0.7090... Generator Loss: 1.9564
Epoch 8/10... Discriminator Loss: 0.6379... Generator Loss: 1.7907
Epoch 8/10... Discriminator Loss: 0.7600... Generator Loss: 1.5224
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.6538... Generator Loss: 2.3061
Epoch 8/10... Discriminator Loss: 0.7994... Generator Loss: 1.6350
Epoch 8/10... Discriminator Loss: 1.2315... Generator Loss: 1.1336
Epoch 8/10... Discriminator Loss: 0.6516... Generator Loss: 2.0254
Epoch 8/10... Discriminator Loss: 0.5916... Generator Loss: 2.7415
Epoch 8/10... Discriminator Loss: 0.5417... Generator Loss: 3.7687
Epoch 8/10... Discriminator Loss: 0.6285... Generator Loss: 2.2756
Epoch 8/10... Discriminator Loss: 0.6619... Generator Loss: 2.4615
Epoch 8/10... Discriminator Loss: 0.5629... Generator Loss: 2.7834
Epoch 8/10... Discriminator Loss: 0.6289... Generator Loss: 1.9251
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.6606... Generator Loss: 1.8933
Epoch 8/10... Discriminator Loss: 0.6559... Generator Loss: 1.5749
Epoch 8/10... Discriminator Loss: 0.7844... Generator Loss: 1.5534
Epoch 8/10... Discriminator Loss: 0.5046... Generator Loss: 2.6991
Epoch 8/10... Discriminator Loss: 0.7780... Generator Loss: 3.5184
Epoch 8/10... Discriminator Loss: 0.5169... Generator Loss: 1.9458
Epoch 8/10... Discriminator Loss: 0.7432... Generator Loss: 1.9882
Epoch 8/10... Discriminator Loss: 0.6872... Generator Loss: 2.9640
Epoch 8/10... Discriminator Loss: 0.5978... Generator Loss: 1.5403
Epoch 8/10... Discriminator Loss: 0.8209... Generator Loss: 4.0545
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.5936... Generator Loss: 1.9650
Epoch 8/10... Discriminator Loss: 0.5794... Generator Loss: 2.8241
Epoch 8/10... Discriminator Loss: 0.8670... Generator Loss: 1.1233
Epoch 8/10... Discriminator Loss: 0.8517... Generator Loss: 1.7048
Epoch 8/10... Discriminator Loss: 0.8995... Generator Loss: 1.2943
Epoch 8/10... Discriminator Loss: 0.4407... Generator Loss: 2.7450
Epoch 8/10... Discriminator Loss: 0.6004... Generator Loss: 1.7278
Epoch 8/10... Discriminator Loss: 0.6553... Generator Loss: 2.5553
Epoch 8/10... Discriminator Loss: 0.7624... Generator Loss: 1.9318
Epoch 8/10... Discriminator Loss: 0.6313... Generator Loss: 1.7888
<class 'numpy.ndarray'>
Epoch 8/10... Discriminator Loss: 0.9250... Generator Loss: 0.8887
Epoch 8/10... Discriminator Loss: 0.5677... Generator Loss: 2.2678
Epoch 8/10... Discriminator Loss: 0.7755... Generator Loss: 1.6705
Epoch 8/10... Discriminator Loss: 0.4231... Generator Loss: 3.7199
Epoch 8/10... Discriminator Loss: 1.1244... Generator Loss: 2.4037
Epoch 9/10... Discriminator Loss: 0.6334... Generator Loss: 2.2260
Epoch 9/10... Discriminator Loss: 0.6183... Generator Loss: 2.7187
Epoch 9/10... Discriminator Loss: 0.5107... Generator Loss: 2.1656
Epoch 9/10... Discriminator Loss: 0.7408... Generator Loss: 1.6731
Epoch 9/10... Discriminator Loss: 0.8227... Generator Loss: 1.1375
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.7443... Generator Loss: 3.0716
Epoch 9/10... Discriminator Loss: 0.5176... Generator Loss: 3.2327
Epoch 9/10... Discriminator Loss: 0.7087... Generator Loss: 1.6369
Epoch 9/10... Discriminator Loss: 0.8160... Generator Loss: 3.0616
Epoch 9/10... Discriminator Loss: 0.7906... Generator Loss: 1.5439
Epoch 9/10... Discriminator Loss: 0.4669... Generator Loss: 2.4551
Epoch 9/10... Discriminator Loss: 0.6335... Generator Loss: 2.4154
Epoch 9/10... Discriminator Loss: 0.5910... Generator Loss: 2.3540
Epoch 9/10... Discriminator Loss: 0.5876... Generator Loss: 2.3470
Epoch 9/10... Discriminator Loss: 0.4156... Generator Loss: 3.3504
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5854... Generator Loss: 2.4072
Epoch 9/10... Discriminator Loss: 0.6222... Generator Loss: 1.7165
Epoch 9/10... Discriminator Loss: 0.9152... Generator Loss: 1.0423
Epoch 9/10... Discriminator Loss: 0.4560... Generator Loss: 3.3150
Epoch 9/10... Discriminator Loss: 0.5964... Generator Loss: 2.6807
Epoch 9/10... Discriminator Loss: 0.8011... Generator Loss: 1.5461
Epoch 9/10... Discriminator Loss: 0.8858... Generator Loss: 3.7051
Epoch 9/10... Discriminator Loss: 0.8675... Generator Loss: 1.4752
Epoch 9/10... Discriminator Loss: 0.6507... Generator Loss: 1.7865
Epoch 9/10... Discriminator Loss: 0.6202... Generator Loss: 3.9132
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.8427... Generator Loss: 1.2389
Epoch 9/10... Discriminator Loss: 0.9600... Generator Loss: 1.2907
Epoch 9/10... Discriminator Loss: 0.5893... Generator Loss: 2.2954
Epoch 9/10... Discriminator Loss: 0.7030... Generator Loss: 1.2939
Epoch 9/10... Discriminator Loss: 0.8624... Generator Loss: 1.5092
Epoch 9/10... Discriminator Loss: 1.1637... Generator Loss: 0.6298
Epoch 9/10... Discriminator Loss: 1.1026... Generator Loss: 0.9347
Epoch 9/10... Discriminator Loss: 0.7511... Generator Loss: 1.1955
Epoch 9/10... Discriminator Loss: 0.4846... Generator Loss: 3.4091
Epoch 9/10... Discriminator Loss: 0.8574... Generator Loss: 1.0490
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5229... Generator Loss: 2.7608
Epoch 9/10... Discriminator Loss: 0.5313... Generator Loss: 2.4826
Epoch 9/10... Discriminator Loss: 0.4525... Generator Loss: 3.4789
Epoch 9/10... Discriminator Loss: 0.6123... Generator Loss: 1.7079
Epoch 9/10... Discriminator Loss: 0.5458... Generator Loss: 1.6553
Epoch 9/10... Discriminator Loss: 0.5538... Generator Loss: 3.6110
Epoch 9/10... Discriminator Loss: 0.5722... Generator Loss: 2.5885
Epoch 9/10... Discriminator Loss: 0.5056... Generator Loss: 4.3592
Epoch 9/10... Discriminator Loss: 0.9890... Generator Loss: 1.0824
Epoch 9/10... Discriminator Loss: 1.0020... Generator Loss: 0.6243
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 1.2275... Generator Loss: 2.3783
Epoch 9/10... Discriminator Loss: 0.6143... Generator Loss: 1.9642
Epoch 9/10... Discriminator Loss: 0.5271... Generator Loss: 1.7410
Epoch 9/10... Discriminator Loss: 0.8561... Generator Loss: 1.6259
Epoch 9/10... Discriminator Loss: 0.6262... Generator Loss: 2.4974
Epoch 9/10... Discriminator Loss: 0.7378... Generator Loss: 1.4147
Epoch 9/10... Discriminator Loss: 0.9747... Generator Loss: 1.4263
Epoch 9/10... Discriminator Loss: 0.6273... Generator Loss: 2.4846
Epoch 9/10... Discriminator Loss: 0.5415... Generator Loss: 2.4044
Epoch 9/10... Discriminator Loss: 0.4598... Generator Loss: 3.8681
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.8275... Generator Loss: 1.6240
Epoch 9/10... Discriminator Loss: 0.5202... Generator Loss: 2.6547
Epoch 9/10... Discriminator Loss: 0.6193... Generator Loss: 1.7125
Epoch 9/10... Discriminator Loss: 0.5023... Generator Loss: 3.2291
Epoch 9/10... Discriminator Loss: 0.5693... Generator Loss: 1.7047
Epoch 9/10... Discriminator Loss: 0.4911... Generator Loss: 2.8853
Epoch 9/10... Discriminator Loss: 0.6825... Generator Loss: 2.0798
Epoch 9/10... Discriminator Loss: 1.0987... Generator Loss: 0.9226
Epoch 9/10... Discriminator Loss: 0.4445... Generator Loss: 3.4388
Epoch 9/10... Discriminator Loss: 0.5702... Generator Loss: 2.1102
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5791... Generator Loss: 1.9336
Epoch 9/10... Discriminator Loss: 1.0507... Generator Loss: 0.9816
Epoch 9/10... Discriminator Loss: 0.4864... Generator Loss: 3.0100
Epoch 9/10... Discriminator Loss: 0.5602... Generator Loss: 2.3253
Epoch 9/10... Discriminator Loss: 0.9570... Generator Loss: 1.1313
Epoch 9/10... Discriminator Loss: 0.7009... Generator Loss: 1.5197
Epoch 9/10... Discriminator Loss: 0.7964... Generator Loss: 1.8326
Epoch 9/10... Discriminator Loss: 1.0392... Generator Loss: 1.2458
Epoch 9/10... Discriminator Loss: 0.6086... Generator Loss: 2.6960
Epoch 9/10... Discriminator Loss: 0.7612... Generator Loss: 5.1120
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5446... Generator Loss: 3.0462
Epoch 9/10... Discriminator Loss: 0.8686... Generator Loss: 1.4675
Epoch 9/10... Discriminator Loss: 0.6848... Generator Loss: 1.0175
Epoch 9/10... Discriminator Loss: 0.5907... Generator Loss: 2.4570
Epoch 9/10... Discriminator Loss: 0.6235... Generator Loss: 2.3249
Epoch 9/10... Discriminator Loss: 0.5664... Generator Loss: 3.0230
Epoch 9/10... Discriminator Loss: 0.7837... Generator Loss: 1.0040
Epoch 9/10... Discriminator Loss: 0.7237... Generator Loss: 3.7438
Epoch 9/10... Discriminator Loss: 0.5389... Generator Loss: 3.2964
Epoch 9/10... Discriminator Loss: 0.5030... Generator Loss: 2.9861
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5356... Generator Loss: 2.0797
Epoch 9/10... Discriminator Loss: 0.6787... Generator Loss: 2.5668
Epoch 9/10... Discriminator Loss: 1.0126... Generator Loss: 1.3612
Epoch 9/10... Discriminator Loss: 0.5683... Generator Loss: 1.7775
Epoch 9/10... Discriminator Loss: 0.4557... Generator Loss: 3.4318
Epoch 9/10... Discriminator Loss: 0.6810... Generator Loss: 1.7459
Epoch 9/10... Discriminator Loss: 0.9041... Generator Loss: 1.4605
Epoch 9/10... Discriminator Loss: 0.4451... Generator Loss: 3.5836
Epoch 9/10... Discriminator Loss: 1.3376... Generator Loss: 0.8835
Epoch 9/10... Discriminator Loss: 1.3483... Generator Loss: 0.8484
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5585... Generator Loss: 2.7134
Epoch 9/10... Discriminator Loss: 0.5198... Generator Loss: 1.7811
Epoch 9/10... Discriminator Loss: 0.5415... Generator Loss: 2.6946
Epoch 9/10... Discriminator Loss: 0.5685... Generator Loss: 2.0918
Epoch 9/10... Discriminator Loss: 0.7552... Generator Loss: 2.6247
Epoch 9/10... Discriminator Loss: 0.6145... Generator Loss: 2.9127
Epoch 9/10... Discriminator Loss: 0.5091... Generator Loss: 2.5754
Epoch 9/10... Discriminator Loss: 0.6535... Generator Loss: 3.8242
Epoch 9/10... Discriminator Loss: 0.4800... Generator Loss: 3.0405
Epoch 9/10... Discriminator Loss: 0.8041... Generator Loss: 1.3033
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5404... Generator Loss: 2.5961
Epoch 9/10... Discriminator Loss: 0.4718... Generator Loss: 2.0927
Epoch 9/10... Discriminator Loss: 0.7134... Generator Loss: 1.9206
Epoch 9/10... Discriminator Loss: 0.7206... Generator Loss: 3.0292
Epoch 9/10... Discriminator Loss: 0.8992... Generator Loss: 1.9931
Epoch 9/10... Discriminator Loss: 0.6368... Generator Loss: 1.5807
Epoch 9/10... Discriminator Loss: 0.5559... Generator Loss: 2.3920
Epoch 9/10... Discriminator Loss: 0.6466... Generator Loss: 1.9196
Epoch 9/10... Discriminator Loss: 1.1224... Generator Loss: 5.1831
Epoch 9/10... Discriminator Loss: 0.5722... Generator Loss: 2.4751
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5978... Generator Loss: 2.1444
Epoch 9/10... Discriminator Loss: 0.7203... Generator Loss: 1.7108
Epoch 9/10... Discriminator Loss: 0.6041... Generator Loss: 2.1657
Epoch 9/10... Discriminator Loss: 0.5437... Generator Loss: 1.8941
Epoch 9/10... Discriminator Loss: 0.6713... Generator Loss: 1.9004
Epoch 9/10... Discriminator Loss: 0.8585... Generator Loss: 3.8839
Epoch 9/10... Discriminator Loss: 1.1257... Generator Loss: 1.2987
Epoch 9/10... Discriminator Loss: 0.8609... Generator Loss: 1.3171
Epoch 9/10... Discriminator Loss: 0.4477... Generator Loss: 2.9038
Epoch 9/10... Discriminator Loss: 0.4876... Generator Loss: 2.3023
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.6461... Generator Loss: 1.8536
Epoch 9/10... Discriminator Loss: 0.5146... Generator Loss: 2.8768
Epoch 9/10... Discriminator Loss: 0.6641... Generator Loss: 2.5651
Epoch 9/10... Discriminator Loss: 0.8875... Generator Loss: 1.2948
Epoch 9/10... Discriminator Loss: 0.6693... Generator Loss: 1.9311
Epoch 9/10... Discriminator Loss: 0.4895... Generator Loss: 2.9649
Epoch 9/10... Discriminator Loss: 0.9625... Generator Loss: 1.5779
Epoch 9/10... Discriminator Loss: 0.8611... Generator Loss: 1.8337
Epoch 9/10... Discriminator Loss: 0.7615... Generator Loss: 1.9223
Epoch 9/10... Discriminator Loss: 0.5246... Generator Loss: 2.1490
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.5759... Generator Loss: 2.9418
Epoch 9/10... Discriminator Loss: 0.5634... Generator Loss: 1.5649
Epoch 9/10... Discriminator Loss: 1.2388... Generator Loss: 0.9761
Epoch 9/10... Discriminator Loss: 0.6136... Generator Loss: 2.1793
Epoch 9/10... Discriminator Loss: 0.6793... Generator Loss: 1.6756
Epoch 9/10... Discriminator Loss: 0.7019... Generator Loss: 2.1276
Epoch 9/10... Discriminator Loss: 0.4992... Generator Loss: 3.3661
Epoch 9/10... Discriminator Loss: 0.9333... Generator Loss: 1.3863
Epoch 9/10... Discriminator Loss: 0.5566... Generator Loss: 3.8795
Epoch 9/10... Discriminator Loss: 0.8632... Generator Loss: 3.5105
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 1.0142... Generator Loss: 1.3283
Epoch 9/10... Discriminator Loss: 0.5775... Generator Loss: 2.5381
Epoch 9/10... Discriminator Loss: 0.4388... Generator Loss: 2.7549
Epoch 9/10... Discriminator Loss: 0.8427... Generator Loss: 1.1707
Epoch 9/10... Discriminator Loss: 0.4739... Generator Loss: 3.2851
Epoch 9/10... Discriminator Loss: 0.6780... Generator Loss: 1.3215
Epoch 9/10... Discriminator Loss: 0.6488... Generator Loss: 2.3160
Epoch 9/10... Discriminator Loss: 0.5999... Generator Loss: 2.9485
Epoch 9/10... Discriminator Loss: 0.7513... Generator Loss: 1.2195
Epoch 9/10... Discriminator Loss: 0.4975... Generator Loss: 2.3672
<class 'numpy.ndarray'>
Epoch 9/10... Discriminator Loss: 0.8281... Generator Loss: 3.0998
Epoch 9/10... Discriminator Loss: 0.6349... Generator Loss: 3.0883
Epoch 9/10... Discriminator Loss: 0.6289... Generator Loss: 3.9388
Epoch 10/10... Discriminator Loss: 0.6039... Generator Loss: 2.4538
Epoch 10/10... Discriminator Loss: 0.5851... Generator Loss: 1.9712
Epoch 10/10... Discriminator Loss: 0.6379... Generator Loss: 1.5102
Epoch 10/10... Discriminator Loss: 0.7346... Generator Loss: 3.0308
Epoch 10/10... Discriminator Loss: 0.6604... Generator Loss: 1.5851
Epoch 10/10... Discriminator Loss: 0.5675... Generator Loss: 2.1216
Epoch 10/10... Discriminator Loss: 0.4557... Generator Loss: 3.1275
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.8241... Generator Loss: 1.3168
Epoch 10/10... Discriminator Loss: 0.6136... Generator Loss: 3.2460
Epoch 10/10... Discriminator Loss: 0.5382... Generator Loss: 3.0721
Epoch 10/10... Discriminator Loss: 0.9079... Generator Loss: 0.9683
Epoch 10/10... Discriminator Loss: 0.4755... Generator Loss: 2.8094
Epoch 10/10... Discriminator Loss: 0.5858... Generator Loss: 2.4676
Epoch 10/10... Discriminator Loss: 1.3309... Generator Loss: 0.6920
Epoch 10/10... Discriminator Loss: 1.0896... Generator Loss: 1.2225
Epoch 10/10... Discriminator Loss: 0.6898... Generator Loss: 1.5377
Epoch 10/10... Discriminator Loss: 0.5392... Generator Loss: 3.3332
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.5918... Generator Loss: 2.0098
Epoch 10/10... Discriminator Loss: 0.6700... Generator Loss: 2.0568
Epoch 10/10... Discriminator Loss: 0.4901... Generator Loss: 1.6246
Epoch 10/10... Discriminator Loss: 1.6474... Generator Loss: 0.9622
Epoch 10/10... Discriminator Loss: 0.8498... Generator Loss: 1.4248
Epoch 10/10... Discriminator Loss: 1.0829... Generator Loss: 1.1630
Epoch 10/10... Discriminator Loss: 0.8970... Generator Loss: 1.5860
Epoch 10/10... Discriminator Loss: 0.7742... Generator Loss: 1.6165
Epoch 10/10... Discriminator Loss: 0.5968... Generator Loss: 3.0550
Epoch 10/10... Discriminator Loss: 0.5868... Generator Loss: 3.2066
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.4587... Generator Loss: 2.4394
Epoch 10/10... Discriminator Loss: 0.6541... Generator Loss: 1.9162
Epoch 10/10... Discriminator Loss: 0.6174... Generator Loss: 2.2017
Epoch 10/10... Discriminator Loss: 0.5565... Generator Loss: 2.5065
Epoch 10/10... Discriminator Loss: 0.5488... Generator Loss: 3.2654
Epoch 10/10... Discriminator Loss: 0.4523... Generator Loss: 3.6176
Epoch 10/10... Discriminator Loss: 0.4352... Generator Loss: 2.8739
Epoch 10/10... Discriminator Loss: 0.4678... Generator Loss: 2.9124
Epoch 10/10... Discriminator Loss: 0.6913... Generator Loss: 1.7376
Epoch 10/10... Discriminator Loss: 0.5426... Generator Loss: 2.5811
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.5933... Generator Loss: 3.0113
Epoch 10/10... Discriminator Loss: 0.6267... Generator Loss: 2.4231
Epoch 10/10... Discriminator Loss: 0.5191... Generator Loss: 2.8531
Epoch 10/10... Discriminator Loss: 0.6443... Generator Loss: 0.9778
Epoch 10/10... Discriminator Loss: 0.5849... Generator Loss: 1.7427
Epoch 10/10... Discriminator Loss: 0.7275... Generator Loss: 1.5920
Epoch 10/10... Discriminator Loss: 0.6911... Generator Loss: 1.7095
Epoch 10/10... Discriminator Loss: 0.4737... Generator Loss: 2.5779
Epoch 10/10... Discriminator Loss: 0.9092... Generator Loss: 1.2686
Epoch 10/10... Discriminator Loss: 0.4700... Generator Loss: 4.2541
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.5667... Generator Loss: 1.8605
Epoch 10/10... Discriminator Loss: 0.7678... Generator Loss: 1.7268
Epoch 10/10... Discriminator Loss: 0.6446... Generator Loss: 2.3908
Epoch 10/10... Discriminator Loss: 0.5361... Generator Loss: 2.2209
Epoch 10/10... Discriminator Loss: 0.5234... Generator Loss: 3.2814
Epoch 10/10... Discriminator Loss: 0.8658... Generator Loss: 1.4405
Epoch 10/10... Discriminator Loss: 0.6488... Generator Loss: 2.6007
Epoch 10/10... Discriminator Loss: 0.6780... Generator Loss: 2.3202
Epoch 10/10... Discriminator Loss: 0.7764... Generator Loss: 1.9052
Epoch 10/10... Discriminator Loss: 0.5404... Generator Loss: 2.5804
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.6179... Generator Loss: 1.9156
Epoch 10/10... Discriminator Loss: 1.2676... Generator Loss: 0.5553
Epoch 10/10... Discriminator Loss: 0.8574... Generator Loss: 4.5932
Epoch 10/10... Discriminator Loss: 0.5420... Generator Loss: 3.1917
Epoch 10/10... Discriminator Loss: 0.7063... Generator Loss: 2.2786
Epoch 10/10... Discriminator Loss: 0.6492... Generator Loss: 2.4146
Epoch 10/10... Discriminator Loss: 0.8762... Generator Loss: 1.5051
Epoch 10/10... Discriminator Loss: 0.4488... Generator Loss: 3.0929
Epoch 10/10... Discriminator Loss: 0.9117... Generator Loss: 3.5753
Epoch 10/10... Discriminator Loss: 0.6938... Generator Loss: 2.2395
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.9980... Generator Loss: 0.9503
Epoch 10/10... Discriminator Loss: 0.7943... Generator Loss: 2.1302
Epoch 10/10... Discriminator Loss: 0.8286... Generator Loss: 3.7861
Epoch 10/10... Discriminator Loss: 0.5248... Generator Loss: 2.5673
Epoch 10/10... Discriminator Loss: 0.4412... Generator Loss: 1.8075
Epoch 10/10... Discriminator Loss: 1.1507... Generator Loss: 0.6136
Epoch 10/10... Discriminator Loss: 0.4891... Generator Loss: 2.4373
Epoch 10/10... Discriminator Loss: 0.5623... Generator Loss: 2.2962
Epoch 10/10... Discriminator Loss: 0.6639... Generator Loss: 1.5491
Epoch 10/10... Discriminator Loss: 0.5625... Generator Loss: 2.8221
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.5751... Generator Loss: 2.0715
Epoch 10/10... Discriminator Loss: 0.6813... Generator Loss: 2.4941
Epoch 10/10... Discriminator Loss: 0.7300... Generator Loss: 2.5307
Epoch 10/10... Discriminator Loss: 0.5221... Generator Loss: 3.8303
Epoch 10/10... Discriminator Loss: 0.6815... Generator Loss: 1.6661
Epoch 10/10... Discriminator Loss: 0.5981... Generator Loss: 2.0729
Epoch 10/10... Discriminator Loss: 0.6056... Generator Loss: 1.5070
Epoch 10/10... Discriminator Loss: 0.4883... Generator Loss: 2.9173
Epoch 10/10... Discriminator Loss: 0.7199... Generator Loss: 1.6827
Epoch 10/10... Discriminator Loss: 0.8301... Generator Loss: 1.5558
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.5758... Generator Loss: 3.3967
Epoch 10/10... Discriminator Loss: 0.5998... Generator Loss: 3.0217
Epoch 10/10... Discriminator Loss: 0.4538... Generator Loss: 3.9235
Epoch 10/10... Discriminator Loss: 0.7211... Generator Loss: 2.7050
Epoch 10/10... Discriminator Loss: 0.6847... Generator Loss: 2.6783
Epoch 10/10... Discriminator Loss: 0.6317... Generator Loss: 1.4645
Epoch 10/10... Discriminator Loss: 0.7588... Generator Loss: 1.5225
Epoch 10/10... Discriminator Loss: 0.7396... Generator Loss: 1.5516
Epoch 10/10... Discriminator Loss: 0.8962... Generator Loss: 3.1335
Epoch 10/10... Discriminator Loss: 0.5108... Generator Loss: 2.3558
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.6593... Generator Loss: 2.4494
Epoch 10/10... Discriminator Loss: 0.5364... Generator Loss: 3.2241
Epoch 10/10... Discriminator Loss: 0.7315... Generator Loss: 2.7537
Epoch 10/10... Discriminator Loss: 0.6437... Generator Loss: 2.6455
Epoch 10/10... Discriminator Loss: 0.6899... Generator Loss: 3.1951
Epoch 10/10... Discriminator Loss: 0.6237... Generator Loss: 1.8183
Epoch 10/10... Discriminator Loss: 0.5580... Generator Loss: 2.5678
Epoch 10/10... Discriminator Loss: 0.4853... Generator Loss: 3.0794
Epoch 10/10... Discriminator Loss: 0.4767... Generator Loss: 2.5137
Epoch 10/10... Discriminator Loss: 0.4982... Generator Loss: 3.2115
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.6558... Generator Loss: 1.7606
Epoch 10/10... Discriminator Loss: 0.6828... Generator Loss: 1.7931
Epoch 10/10... Discriminator Loss: 1.1842... Generator Loss: 3.3509
Epoch 10/10... Discriminator Loss: 0.7133... Generator Loss: 1.3609
Epoch 10/10... Discriminator Loss: 0.8415... Generator Loss: 2.5511
Epoch 10/10... Discriminator Loss: 1.4503... Generator Loss: 0.9993
Epoch 10/10... Discriminator Loss: 1.0232... Generator Loss: 3.5031
Epoch 10/10... Discriminator Loss: 0.5241... Generator Loss: 2.4940
Epoch 10/10... Discriminator Loss: 0.6576... Generator Loss: 3.1456
Epoch 10/10... Discriminator Loss: 0.5689... Generator Loss: 1.8173
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.6573... Generator Loss: 1.7914
Epoch 10/10... Discriminator Loss: 0.4712... Generator Loss: 3.3187
Epoch 10/10... Discriminator Loss: 0.5297... Generator Loss: 3.0703
Epoch 10/10... Discriminator Loss: 0.6772... Generator Loss: 2.0370
Epoch 10/10... Discriminator Loss: 0.5230... Generator Loss: 2.0457
Epoch 10/10... Discriminator Loss: 0.6263... Generator Loss: 2.3930
Epoch 10/10... Discriminator Loss: 0.4657... Generator Loss: 2.2038
Epoch 10/10... Discriminator Loss: 0.4884... Generator Loss: 3.8357
Epoch 10/10... Discriminator Loss: 0.4692... Generator Loss: 2.0601
Epoch 10/10... Discriminator Loss: 0.5605... Generator Loss: 2.3815
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 1.3026... Generator Loss: 0.4406
Epoch 10/10... Discriminator Loss: 0.4609... Generator Loss: 3.2151
Epoch 10/10... Discriminator Loss: 0.5896... Generator Loss: 2.4724
Epoch 10/10... Discriminator Loss: 0.6109... Generator Loss: 2.1776
Epoch 10/10... Discriminator Loss: 0.7429... Generator Loss: 2.4496
Epoch 10/10... Discriminator Loss: 0.5977... Generator Loss: 2.2139
Epoch 10/10... Discriminator Loss: 0.4800... Generator Loss: 3.0574
Epoch 10/10... Discriminator Loss: 0.5963... Generator Loss: 2.2004
Epoch 10/10... Discriminator Loss: 0.7159... Generator Loss: 1.6449
Epoch 10/10... Discriminator Loss: 0.4244... Generator Loss: 3.3119
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.7140... Generator Loss: 2.7417
Epoch 10/10... Discriminator Loss: 0.8511... Generator Loss: 1.7804
Epoch 10/10... Discriminator Loss: 0.4557... Generator Loss: 2.6117
Epoch 10/10... Discriminator Loss: 0.5936... Generator Loss: 2.2458
Epoch 10/10... Discriminator Loss: 0.5440... Generator Loss: 1.9582
Epoch 10/10... Discriminator Loss: 1.1456... Generator Loss: 1.1369
Epoch 10/10... Discriminator Loss: 0.8428... Generator Loss: 1.4608
Epoch 10/10... Discriminator Loss: 0.6545... Generator Loss: 2.5674
Epoch 10/10... Discriminator Loss: 0.6430... Generator Loss: 4.1449
Epoch 10/10... Discriminator Loss: 0.7055... Generator Loss: 1.5444
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.6927... Generator Loss: 2.5248
Epoch 10/10... Discriminator Loss: 0.5200... Generator Loss: 3.3502
Epoch 10/10... Discriminator Loss: 0.4992... Generator Loss: 2.4128
Epoch 10/10... Discriminator Loss: 0.6424... Generator Loss: 2.1309
Epoch 10/10... Discriminator Loss: 0.8052... Generator Loss: 1.5523
Epoch 10/10... Discriminator Loss: 0.6748... Generator Loss: 1.5971
Epoch 10/10... Discriminator Loss: 0.6676... Generator Loss: 3.5468
Epoch 10/10... Discriminator Loss: 0.6025... Generator Loss: 3.6897
Epoch 10/10... Discriminator Loss: 1.0543... Generator Loss: 1.3701
Epoch 10/10... Discriminator Loss: 0.8355... Generator Loss: 2.1400
<class 'numpy.ndarray'>
Epoch 10/10... Discriminator Loss: 0.7934... Generator Loss: 1.4599
Epoch 10/10... Discriminator Loss: 0.5481... Generator Loss: 3.8698

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

In [ ]: